mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 02:02:46 +00:00
add fp16xf4 moe
This commit is contained in:
Feng Shijie
@@ -1,4 +1,6 @@
|
||||
add_executable(tile_example_moe_flatmm EXCLUDE_FROM_ALL moe_flatmm.cpp)
|
||||
add_executable(tile_example_a16w4_moe_flatmm EXCLUDE_FROM_ALL mixed_prec/a16w4_moe_flatmm.cpp)
|
||||
|
||||
|
||||
set(EXAMPLE_MOE_FLATMM_COMPILE_OPTIONS)
|
||||
|
||||
@@ -9,3 +11,4 @@ endif()
|
||||
|
||||
list(APPEND EXAMPLE_MOE_FLATMM_COMPILE_OPTIONS --save-temps)
|
||||
target_compile_options(tile_example_moe_flatmm PRIVATE ${EXAMPLE_MOE_FLATMM_COMPILE_OPTIONS})
|
||||
target_compile_options(tile_example_a16w4_moe_flatmm PRIVATE ${EXAMPLE_MOE_FLATMM_COMPILE_OPTIONS})
|
||||
|
||||
534
example/ck_tile/21_moe_flatmm/mixed_prec/a16w4_moe_flatmm.cpp
Normal file
534
example/ck_tile/21_moe_flatmm/mixed_prec/a16w4_moe_flatmm.cpp
Normal file
@@ -0,0 +1,534 @@
|
||||
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <hip/hip_runtime.h>
|
||||
|
||||
#include <cstring>
|
||||
#include <iostream>
|
||||
#include <ostream>
|
||||
#include <string>
|
||||
#include <tuple>
|
||||
#include <memory>
|
||||
|
||||
#include "a16w4_moe_flatmm.hpp"
|
||||
|
||||
#include "ck_tile/core.hpp"
|
||||
#include "ck_tile/ops/epilogue.hpp"
|
||||
#include "ck_tile/ops/gemm.hpp"
|
||||
#include "ck_tile/ops/flatmm.hpp"
|
||||
#include "ck_tile/ops/moe_flatmm.hpp"
|
||||
#include "ck_tile/host.hpp"
|
||||
#include "ck_tile/host/reference/reference_moe_gemm.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>>{};
|
||||
}
|
||||
|
||||
// gemm1
|
||||
// operand-A = [num_token, d_model]
|
||||
// operand-B = [num_expert, hidden, d_model]
|
||||
// operand-C = [num_token, topk, hidden]
|
||||
|
||||
// gemm2
|
||||
// operand-A = [num_token, topk, hidden]
|
||||
// operand-B = [num_expert, d_model, hidden]
|
||||
// operand-C = [num_token, d_model]
|
||||
|
||||
template <typename FlatmmConfig,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDatatype,
|
||||
typename AccDataType,
|
||||
typename CDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
ck_tile::MoeFlatmmKind moe_kind = ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_only,
|
||||
typename CDEElementWise = ck_tile::element_wise::PassThrough,
|
||||
typename ScaleM,
|
||||
typename ScaleN>
|
||||
float a16w4_moe_gemm(const ck_tile::MoeFlatmmHostArgs<ScaleM, ScaleN>& args,
|
||||
const ck_tile::stream_config& s)
|
||||
{
|
||||
using CodegenFlatmmShape = 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<CodegenFlatmmShape,
|
||||
FlatmmConfig::TileParitionerGroupNum,
|
||||
FlatmmConfig::TileParitionerM01>;
|
||||
|
||||
using Traits = ck_tile::TileGemmTraits<FlatmmConfig::kPadM,
|
||||
FlatmmConfig::kPadN,
|
||||
FlatmmConfig::kPadK,
|
||||
ALayout,
|
||||
BLayout,
|
||||
ELayout,
|
||||
FlatmmConfig::NumWaveGroups>;
|
||||
|
||||
using CodegenGemmTraits = ck_tile::TileGemmUniversalTraits<FlatmmConfig::kPadM,
|
||||
FlatmmConfig::kPadN,
|
||||
FlatmmConfig::kPadK,
|
||||
FlatmmConfig::DoubleSmemBuffer,
|
||||
ALayout,
|
||||
BLayout,
|
||||
ELayout,
|
||||
FlatmmConfig::TransposeC,
|
||||
FlatmmConfig::UseStructuredSparsity,
|
||||
false, // UsePersistentKernel_
|
||||
FlatmmConfig::NumWaveGroups,
|
||||
true>; // Preshuffle_
|
||||
|
||||
constexpr bool MXFP4_Pipeline = std::is_same_v<BDataType, ck_tile::pk_fp4_t>;
|
||||
|
||||
if constexpr(!MXFP4_Pipeline && moe_kind == ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up)
|
||||
{
|
||||
static_assert(
|
||||
FlatmmConfig::N_Tile % (FlatmmConfig::N_Warp * FlatmmConfig::N_Warp_Tile * 2) == 0,
|
||||
"requires NRepeat is multiple of 2 for FFN_gemm1_gate_up");
|
||||
}
|
||||
|
||||
using ComputeDataType = ADataType;
|
||||
static_assert(sizeof(ComputeDataType) >= sizeof(BDataType),
|
||||
"mixed_prec_flatmm requires ADataType is a wider type than BDataType");
|
||||
|
||||
using GemmPipelineProblem = ck_tile::GemmPipelineProblem<ComputeDataType,
|
||||
ComputeDataType,
|
||||
AccDataType,
|
||||
CodegenFlatmmShape,
|
||||
Traits>;
|
||||
|
||||
using BaseGemmPipeline = ck_tile::BaseFlatmmPipelineAGmemBGmemCRegV1<GemmPipelineProblem>;
|
||||
|
||||
const ck_tile::index_t k_grain = args.k_batch * FlatmmConfig::K_Tile;
|
||||
const ck_tile::index_t K_split = (args.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 = BaseGemmPipeline::BlockHasHotloop(num_loop);
|
||||
const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
|
||||
float ave_time{0};
|
||||
|
||||
const auto Run = [&](const auto has_hot_loop_,
|
||||
const auto tail_number_,
|
||||
const auto memory_operation_) {
|
||||
constexpr bool has_hot_loop_v = has_hot_loop_.value;
|
||||
constexpr auto tail_number_v = tail_number_.value;
|
||||
constexpr auto scheduler = FlatmmConfig::Scheduler;
|
||||
constexpr auto memory_operation = memory_operation_.value;
|
||||
|
||||
using CodegenPipelineProblem =
|
||||
std::conditional_t<MXFP4_Pipeline,
|
||||
ck_tile::F16xMXF4FlatmmPipelineProblem<ADataType,
|
||||
BDataType,
|
||||
AccDataType,
|
||||
CodegenFlatmmShape,
|
||||
CodegenGemmTraits,
|
||||
scheduler,
|
||||
has_hot_loop_v,
|
||||
tail_number_v>,
|
||||
ck_tile::FlatmmPipelineProblem<ADataType,
|
||||
BDataType,
|
||||
AccDataType,
|
||||
CodegenFlatmmShape,
|
||||
CodegenGemmTraits,
|
||||
scheduler,
|
||||
has_hot_loop_v,
|
||||
tail_number_v>>;
|
||||
|
||||
constexpr int BlockedXDLN_PerWarp = 2; // determined by scale shuffle pattern
|
||||
|
||||
using GemmEpilogue = ck_tile::CShuffleEpilogue<
|
||||
ck_tile::CShuffleEpilogueProblem<ComputeDataType,
|
||||
ComputeDataType,
|
||||
DsDatatype,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
CDEElementWise,
|
||||
CodegenPipelineProblem::kBlockSize,
|
||||
TilePartitioner::MPerBlock,
|
||||
TilePartitioner::NPerBlock,
|
||||
FlatmmConfig::M_Warp,
|
||||
FlatmmConfig::N_Warp,
|
||||
FlatmmConfig::M_Warp_Tile,
|
||||
FlatmmConfig::N_Warp_Tile,
|
||||
FlatmmConfig::K_Warp_Tile,
|
||||
CodegenPipelineProblem::TransposeC,
|
||||
memory_operation,
|
||||
FlatmmConfig::NumWaveGroups,
|
||||
false,
|
||||
1,
|
||||
FlatmmConfig::TiledMMAPermuteN,
|
||||
BlockedXDLN_PerWarp>>;
|
||||
|
||||
using CodegenFlatmmPipeline = std::conditional_t<
|
||||
MXFP4_Pipeline,
|
||||
ck_tile::F16xMXF4FlatmmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>,
|
||||
ck_tile::MoeFlatmmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>>;
|
||||
|
||||
using Kernel = ck_tile::
|
||||
MoeFlatmmKernel<TilePartitioner, CodegenFlatmmPipeline, GemmEpilogue, moe_kind>;
|
||||
|
||||
auto kargs = Kernel::MakeKernelArgs(args);
|
||||
|
||||
const dim3 grids = Kernel::GridSize(kargs);
|
||||
constexpr dim3 blocks = Kernel::BlockSize();
|
||||
|
||||
if(!Kernel::IsSupportedArgument(kargs))
|
||||
{
|
||||
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
|
||||
}
|
||||
|
||||
if(s.log_level_ > 0)
|
||||
{
|
||||
std::cout << "Launching kernel with args:" << CodegenFlatmmShape::GetName() << "\n"
|
||||
<< "Shape: " << CodegenFlatmmShape::GetName() << "\n"
|
||||
<< "problem: " << CodegenPipelineProblem::GetName() << "\n"
|
||||
<< "pipeline: " << CodegenFlatmmPipeline::GetName() << "\n"
|
||||
<< "grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
|
||||
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z << "}"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
if(s.flush_cache_)
|
||||
{
|
||||
std::cout << "Flushing cache..." << std::endl;
|
||||
static constexpr ck_tile::index_t APackedSize =
|
||||
std::is_same_v<BDataType, ck_tile::pk_int4_t> ||
|
||||
std::is_same_v<BDataType, ck_tile::pk_fp4_t>
|
||||
? 2
|
||||
: 1;
|
||||
static constexpr ck_tile::index_t BPackedSize =
|
||||
std::is_same_v<BDataType, ck_tile::pk_int4_t> ||
|
||||
std::is_same_v<BDataType, ck_tile::pk_fp4_t>
|
||||
? 2
|
||||
: 1;
|
||||
|
||||
ck_tile::HostTensor<ADataType> a_m(ck_tile::host_tensor_descriptor(
|
||||
moe_kind == ck_tile::MoeFlatmmKind::kFFN_gemm2 ? args.NumTokens * args.TopK
|
||||
: args.NumTokens,
|
||||
args.K,
|
||||
args.stride_A,
|
||||
is_row_major(ALayout{})));
|
||||
ck_tile::HostTensor<BDataType> b_n(ck_tile::host_tensor_descriptor(
|
||||
args.K, args.N * args.NumExperts, args.stride_B, is_row_major(BLayout{})));
|
||||
|
||||
const int outputN =
|
||||
moe_kind == ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up ? args.N / 2 : args.N;
|
||||
|
||||
auto size_a_buffer = a_m.get_element_space_size_in_bytes() / APackedSize;
|
||||
auto size_b_buffer = b_n.get_element_space_size_in_bytes() / BPackedSize;
|
||||
|
||||
ck_tile::RotatingMemWrapper<ADataType, BDataType> rotating_mem(
|
||||
kargs.a_ptr, kargs.b_ptr, s.rotating_count_, size_a_buffer, size_b_buffer);
|
||||
rotating_mem.Print();
|
||||
|
||||
auto run_flush_cache = [&]() {
|
||||
// flush icache
|
||||
ck_tile::flush_icache();
|
||||
// rotating mem
|
||||
rotating_mem.Next();
|
||||
// clear c mem
|
||||
if(moe_kind == ck_tile::MoeFlatmmKind::kFFN_gemm2)
|
||||
hipGetErrorString(hipMemsetAsync(
|
||||
args.e_ptr, 0, args.NumTokens * args.N * sizeof(CDataType), s.stream_id_));
|
||||
else if(args.k_batch > 1)
|
||||
hipGetErrorString(
|
||||
hipMemsetAsync(args.e_ptr,
|
||||
0,
|
||||
args.NumTokens * args.TopK * outputN * sizeof(CDataType),
|
||||
s.stream_id_));
|
||||
};
|
||||
ave_time = ck_tile::launch_kernel_preprocess(
|
||||
s,
|
||||
run_flush_cache,
|
||||
ck_tile::make_kernel<blocks.x, FlatmmConfig::kBlockPerCu>(
|
||||
Kernel{}, grids, blocks, 0, kargs));
|
||||
}
|
||||
else
|
||||
{
|
||||
ave_time =
|
||||
ck_tile::launch_kernel(s,
|
||||
ck_tile::make_kernel<blocks.x, FlatmmConfig::kBlockPerCu>(
|
||||
Kernel{}, grids, blocks, 0, kargs));
|
||||
}
|
||||
return ave_time;
|
||||
};
|
||||
|
||||
const auto RunSplitk = [&](const auto has_hot_loop_, const auto tail_number_) {
|
||||
if(args.k_batch == 1)
|
||||
{
|
||||
Run(has_hot_loop_,
|
||||
tail_number_,
|
||||
ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
||||
ck_tile::memory_operation_enum::set>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
Run(has_hot_loop_,
|
||||
tail_number_,
|
||||
ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
||||
ck_tile::memory_operation_enum::atomic_add>{});
|
||||
}
|
||||
};
|
||||
BaseGemmPipeline::TailHandler(RunSplitk, has_hot_loop, tail_num);
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
template <class FlatmmConfig, ck_tile::MoeFlatmmKind moe_kind, class IterSrc, class IterDst>
|
||||
void shuffle_mxfp4_weight(const IterSrc src, IterDst dst, int experts_cnt, int N, int K)
|
||||
{
|
||||
int KPack = 16;
|
||||
int NLane = FlatmmConfig::N_Warp_Tile;
|
||||
int KLane = 64 / NLane;
|
||||
int K_pk = K / 2;
|
||||
int K0 = K_pk / (KLane * KPack);
|
||||
// K -> K0 KLane KPack
|
||||
// N -> N0 NLane
|
||||
// N, K -> N0 K0 KLane NLane KPack
|
||||
int tempk;
|
||||
|
||||
if constexpr(moe_kind == ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up)
|
||||
{
|
||||
int up_stride = N / 2 / NLane;
|
||||
|
||||
for(int eid = 0; eid < experts_cnt; ++eid)
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
for(int k = 0; k < K_pk; ++k)
|
||||
{
|
||||
int n0 = n / NLane;
|
||||
int n1 = n % NLane;
|
||||
|
||||
// interleave gate and up part with granularity is 16.
|
||||
int n0_interleave = n >= N / 2 ? (n0 - up_stride) * 2 + 1 : // up part
|
||||
n0 * 2; // gate part
|
||||
|
||||
int k0 = k / (KLane * KPack);
|
||||
tempk = k % (KLane * KPack);
|
||||
int k1 = tempk / KPack;
|
||||
int k2 = tempk % KPack;
|
||||
|
||||
int outputIndex = eid * N * K_pk + n0_interleave * KPack * NLane * KLane * K0 +
|
||||
k0 * KPack * NLane * KLane + k1 * KPack * NLane + n1 * KPack +
|
||||
k2;
|
||||
|
||||
dst[outputIndex] = src[eid * N * K_pk + n * K_pk + k];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
for(int eid = 0; eid < experts_cnt; ++eid)
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
for(int k = 0; k < K_pk; ++k)
|
||||
{
|
||||
int n0 = n / NLane;
|
||||
int n1 = n % NLane;
|
||||
|
||||
int k0 = k / (KLane * KPack);
|
||||
tempk = k % (KLane * KPack);
|
||||
int k1 = tempk / KPack;
|
||||
int k2 = tempk % KPack;
|
||||
|
||||
int outputIndex = eid * N * K_pk + n0 * KPack * NLane * KLane * K0 +
|
||||
k0 * KPack * NLane * KLane + k1 * KPack * NLane + n1 * KPack +
|
||||
k2;
|
||||
|
||||
dst[outputIndex] = src[eid * N * K_pk + n * K_pk + k];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename FlatmmConfig, ck_tile::MoeFlatmmKind moe_kind, typename T>
|
||||
auto shuffle_mxfp4_scale(const ck_tile::HostTensor<T>& scale, int experts_cnt)
|
||||
{
|
||||
assert(scale.get_lengths().size() == 2);
|
||||
int n_ = scale.get_lengths()[1];
|
||||
int k_ = scale.get_lengths()[0];
|
||||
|
||||
int k_per_expert = k_ / experts_cnt;
|
||||
|
||||
constexpr int K_Pack = 2; // fixed for mxfp4
|
||||
constexpr int N_Pack = 2; // fixed for mxfp4
|
||||
constexpr int GranularityK = 32; // fixed for mxfp4
|
||||
|
||||
constexpr int K_Lane = 64 / FlatmmConfig::N_Warp_Tile; // 4
|
||||
|
||||
static_assert(FlatmmConfig::N_Warp_Tile == 16, "only support XDL_N == 16");
|
||||
static_assert(FlatmmConfig::N_Repeat % N_Pack == 0);
|
||||
static_assert(FlatmmConfig::K_Tile % (K_Pack * K_Lane * GranularityK) == 0);
|
||||
|
||||
if constexpr(moe_kind == ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up)
|
||||
{
|
||||
ck_tile::HostTensor<T> shfl_scale({
|
||||
experts_cnt,
|
||||
k_per_expert / K_Pack / K_Lane,
|
||||
K_Pack,
|
||||
K_Lane,
|
||||
N_Pack, // N_Pack = 2 is composed of Gate + Up.
|
||||
n_ / FlatmmConfig::N_Warp_Tile / N_Pack,
|
||||
FlatmmConfig::N_Warp_Tile,
|
||||
});
|
||||
std::copy(scale.begin(), scale.end(), shfl_scale.begin());
|
||||
return ck_tile::reference_permute(shfl_scale, {0, 5, 1, 3, 6, 2, 4});
|
||||
}
|
||||
else
|
||||
{
|
||||
ck_tile::HostTensor<T> shfl_scale({
|
||||
experts_cnt,
|
||||
k_per_expert / K_Pack / K_Lane,
|
||||
K_Pack,
|
||||
K_Lane,
|
||||
n_ / FlatmmConfig::N_Warp_Tile / N_Pack,
|
||||
N_Pack,
|
||||
FlatmmConfig::N_Warp_Tile,
|
||||
});
|
||||
std::copy(scale.begin(), scale.end(), shfl_scale.begin());
|
||||
return ck_tile::reference_permute(shfl_scale, {0, 4, 1, 3, 6, 2, 5});
|
||||
}
|
||||
}
|
||||
|
||||
#include "run_a16w4_moe_flatmm_example.inc"
|
||||
|
||||
template <typename FlatmmConfig>
|
||||
int run_a16w4_moe_flatmm_example(int argc, char* argv[])
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
if(!result)
|
||||
{
|
||||
return -1;
|
||||
}
|
||||
|
||||
const std::string a_layout = arg_parser.get_str("a_layout");
|
||||
const std::string b_layout = arg_parser.get_str("b_layout");
|
||||
|
||||
const std::string mixed_prec = arg_parser.get_str("mixed_prec");
|
||||
|
||||
using Row = ck_tile::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
if(a_layout == "R" && b_layout == "C")
|
||||
{
|
||||
const std::string gemm_kind = arg_parser.get_str("gemm_kind");
|
||||
if(gemm_kind == "gemm1_gate_up")
|
||||
{
|
||||
if(mixed_prec == "fp16xfp4")
|
||||
{
|
||||
return run_a16w4_moe_gemm_example_with_layouts<
|
||||
ck_tile::half_t,
|
||||
ck_tile::pk_fp4_t,
|
||||
FlatmmConfig,
|
||||
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up>(argc, argv, Row{}, Col{}, Row{});
|
||||
}
|
||||
else if(mixed_prec == "bf16xfp4")
|
||||
{
|
||||
return run_a16w4_moe_gemm_example_with_layouts<
|
||||
ck_tile::bfloat16_t,
|
||||
ck_tile::pk_fp4_t,
|
||||
FlatmmConfig,
|
||||
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up>(argc, argv, Row{}, Col{}, Row{});
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported precision type for gemm1_gate_up!");
|
||||
}
|
||||
}
|
||||
else if(gemm_kind == "gemm1_gate_only")
|
||||
{
|
||||
if(mixed_prec == "fp16xfp4")
|
||||
{
|
||||
return run_a16w4_moe_gemm_example_with_layouts<
|
||||
ck_tile::half_t,
|
||||
ck_tile::pk_fp4_t,
|
||||
FlatmmConfig,
|
||||
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_only>(argc, argv, Row{}, Col{}, Row{});
|
||||
}
|
||||
else if(mixed_prec == "bf16xfp4")
|
||||
{
|
||||
return run_a16w4_moe_gemm_example_with_layouts<
|
||||
ck_tile::bfloat16_t,
|
||||
ck_tile::pk_fp4_t,
|
||||
FlatmmConfig,
|
||||
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_only>(argc, argv, Row{}, Col{}, Row{});
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported precision type for gemm1_gate_only!");
|
||||
}
|
||||
}
|
||||
else if(gemm_kind == "gemm2")
|
||||
{
|
||||
if(mixed_prec == "fp16xfp4")
|
||||
{
|
||||
return run_a16w4_moe_gemm_example_with_layouts<ck_tile::half_t,
|
||||
ck_tile::pk_fp4_t,
|
||||
FlatmmConfig,
|
||||
ck_tile::MoeFlatmmKind::kFFN_gemm2>(
|
||||
argc, argv, Row{}, Col{}, Row{});
|
||||
}
|
||||
else if(mixed_prec == "bf16xfp4")
|
||||
{
|
||||
return run_a16w4_moe_gemm_example_with_layouts<ck_tile::bfloat16_t,
|
||||
ck_tile::pk_fp4_t,
|
||||
FlatmmConfig,
|
||||
ck_tile::MoeFlatmmKind::kFFN_gemm2>(
|
||||
argc, argv, Row{}, Col{}, Row{});
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported precision type for gemm2!");
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unrecoginized gemm_kind parameter, only accept value "
|
||||
"[gemm1_gate_only | gemm1_gate_up | gemm2]");
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported data layout configuration for A,B and C tensors!");
|
||||
}
|
||||
return -1;
|
||||
}
|
||||
|
||||
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_a16w4_moe_flatmm_example<A16W4_FlatmmConfig16>(argc, argv);
|
||||
}
|
||||
// else if(warp_tile == 1)
|
||||
// {
|
||||
// return !run_a16w4_moe_flatmm_example<A16W4_FlatmmConfig16_950>(argc, argv);
|
||||
// }
|
||||
}
|
||||
catch(const std::runtime_error& e)
|
||||
{
|
||||
std::cerr << "Runtime error: " << e.what() << '\n';
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,86 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <string>
|
||||
#include <tuple>
|
||||
|
||||
#include "ck_tile/core.hpp"
|
||||
#include "ck_tile/host/kernel_launch.hpp"
|
||||
#include "ck_tile/ops/moe_flatmm.hpp"
|
||||
|
||||
// GEMM config with 16x16 warp tile
|
||||
struct A16W4_FlatmmConfig16
|
||||
{
|
||||
static constexpr ck_tile::index_t M_Tile = 64;
|
||||
static constexpr ck_tile::index_t N_Tile = 128;
|
||||
static constexpr ck_tile::index_t K_Tile = 256;
|
||||
|
||||
static constexpr ck_tile::index_t M_Warp = 1;
|
||||
static constexpr ck_tile::index_t N_Warp = 4;
|
||||
static constexpr ck_tile::index_t K_Warp = 1;
|
||||
|
||||
static constexpr ck_tile::index_t M_Warp_Tile = 16;
|
||||
static constexpr ck_tile::index_t N_Warp_Tile = 16;
|
||||
static constexpr ck_tile::index_t K_Warp_Tile = 32;
|
||||
|
||||
static constexpr bool kPadM = false;
|
||||
static constexpr bool kPadN = false;
|
||||
static constexpr bool kPadK = false;
|
||||
|
||||
static constexpr bool TransposeC = false;
|
||||
static constexpr bool UseStructuredSparsity = false;
|
||||
|
||||
static constexpr int kBlockPerCu = 1;
|
||||
static constexpr int TileParitionerGroupNum = 8;
|
||||
static constexpr int TileParitionerM01 = 4;
|
||||
static constexpr auto Scheduler = ck_tile::GemmPipelineScheduler::Default;
|
||||
static constexpr ck_tile::index_t NumWaveGroups = 1;
|
||||
static constexpr bool DoubleSmemBuffer = false;
|
||||
|
||||
static constexpr int N_Repeat = N_Tile / N_Warp_Tile / N_Warp;
|
||||
static constexpr bool TiledMMAPermuteN = false;
|
||||
};
|
||||
|
||||
struct A16W4_FlatmmConfig16_950 : public A16W4_FlatmmConfig16
|
||||
{
|
||||
static constexpr ck_tile::index_t N_Tile = 128;
|
||||
static constexpr int kBlockPerCu = 1;
|
||||
|
||||
static constexpr int N_Repeat =
|
||||
N_Tile / A16W4_FlatmmConfig16::N_Warp_Tile / A16W4_FlatmmConfig16::N_Warp;
|
||||
static constexpr bool TiledMMAPermuteN = false;
|
||||
};
|
||||
|
||||
auto create_args(int argc, char* argv[])
|
||||
{
|
||||
ck_tile::ArgParser arg_parser;
|
||||
arg_parser.insert("experts", "8", "Num of experts - 8 by default")
|
||||
.insert("NumTokens", "128", "M dimensions - 128 by default.")
|
||||
.insert("TopK", "3", "Top K - 3 by default.")
|
||||
.insert("N", "4096", "N dimensions - 4096 by default.")
|
||||
.insert("K", "4096", "K dimensions - 4096 by default.")
|
||||
.insert("stride_A", "", "Tensor A strides - it is empty by default.")
|
||||
.insert("stride_B", "", "Tensor B strides - it is empty by default.")
|
||||
.insert("stride_C", "", "Tensor C strides - it is empty by default.")
|
||||
.insert("a_layout", "R", "A tensor data layout - Row by default.")
|
||||
.insert("b_layout", "C", "B tensor data layout - Col by default.")
|
||||
.insert("c_layout", "R", "C tensor data layout - Row by default.")
|
||||
.insert("gemm_kind",
|
||||
"gemm1_gate_only",
|
||||
"Gemm kind in FFN network [gemm1_gate_only | gemm1_gate_up | gemm2] - "
|
||||
"gemm1_gate_only by default.")
|
||||
.insert("validate", "1", "0. No validation, 1. Validation on CPU.")
|
||||
.insert("warmup", "50", "number of iterations before benchmark the kernel")
|
||||
.insert("mixed_prec",
|
||||
"bf16xfp4",
|
||||
"data type for activation and weight, support: bf16xfp4, fp16xfp4")
|
||||
.insert("init", "0", "0:random, 1:constant(1)")
|
||||
.insert(
|
||||
"warp_tile", "0", "0: 16x16, 1: 32x32, 2: 16x16x128 (950 only), 3: 32x32x64 (950 only)")
|
||||
.insert("repeat", "10", "number of iterations to benchmark the kernel.");
|
||||
|
||||
bool result = arg_parser.parse(argc, argv);
|
||||
return std::make_tuple(result, arg_parser);
|
||||
}
|
||||
@@ -0,0 +1,365 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
template <typename FlatmmConfig,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDatatype,
|
||||
typename AccDataType,
|
||||
typename CDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
ck_tile::MoeFlatmmKind kind,
|
||||
typename CDEElementWise = ck_tile::element_wise::PassThrough,
|
||||
typename MoeHostArgs>
|
||||
float invoke_a16w4_moe_gemm(int n_warmup, int n_repeat, const MoeHostArgs& args)
|
||||
{
|
||||
float ave_time = a16w4_moe_gemm<FlatmmConfig,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDatatype,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
kind,
|
||||
CDEElementWise>(
|
||||
args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat, true, true, 50});
|
||||
|
||||
std::string op_name{"Moe Gemm"};
|
||||
|
||||
constexpr int PackedSize = ck_tile::numeric_traits<BDataType>::PackedSize;
|
||||
|
||||
std::size_t flop = std::size_t(2) * args.M * args.N * args.K;
|
||||
std::size_t num_byte = sizeof(ADataType) * args.M * args.K +
|
||||
sizeof(BDataType) * args.N * args.K / PackedSize +
|
||||
sizeof(CDataType) * args.M * args.N;
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
float gb_per_sec = num_byte / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
|
||||
<< gb_per_sec << " GB/s, " << op_name << std::endl;
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
namespace {
|
||||
|
||||
struct LocalSilu
|
||||
{
|
||||
template <typename T>
|
||||
CK_TILE_HOST_DEVICE T operator()(const T& x) const
|
||||
{
|
||||
T y;
|
||||
ck_tile::element_wise::Silu{}(y, x);
|
||||
return y;
|
||||
};
|
||||
};
|
||||
|
||||
} // namespace
|
||||
|
||||
template <typename PrecActType,
|
||||
typename PrecWeightType,
|
||||
typename FlatmmConfig,
|
||||
ck_tile::MoeFlatmmKind kind,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout>
|
||||
int run_a16w4_moe_gemm_example_with_layouts(int argc,
|
||||
char* argv[],
|
||||
const ALayout a_layout = ALayout{},
|
||||
const BLayout b_layout = BLayout{},
|
||||
[[maybe_unused]] const CLayout c_layout = CLayout{})
|
||||
{
|
||||
auto [result, arg_parser] = create_args(argc, argv);
|
||||
|
||||
if(!result)
|
||||
{
|
||||
return -1;
|
||||
};
|
||||
|
||||
using ADataType = PrecActType;
|
||||
using BDataType = PrecWeightType;
|
||||
using CDataType = PrecActType;
|
||||
using AccDataType = float;
|
||||
|
||||
using ScaleType = ck_tile::e8m0_t;
|
||||
|
||||
constexpr int ScaleGranularityN = 1;
|
||||
constexpr int ScaleGranularityK = 32;
|
||||
|
||||
const ck_tile::index_t N = arg_parser.get_int("N");
|
||||
const ck_tile::index_t K = arg_parser.get_int("K");
|
||||
ck_tile::index_t stride_A = arg_parser.get_int("stride_A");
|
||||
ck_tile::index_t stride_B = arg_parser.get_int("stride_B");
|
||||
ck_tile::index_t stride_C = arg_parser.get_int("stride_C");
|
||||
ck_tile::index_t init_method = arg_parser.get_int("init");
|
||||
const ck_tile::index_t num_tokens = arg_parser.get_int("NumTokens");
|
||||
const ck_tile::index_t topk = arg_parser.get_int("TopK");
|
||||
const ck_tile::index_t warmup = arg_parser.get_int("warmup");
|
||||
const ck_tile::index_t repeat = arg_parser.get_int("repeat");
|
||||
const ck_tile::index_t experts = arg_parser.get_int("experts");
|
||||
|
||||
// TODO: replace the magic declaration
|
||||
const ck_tile::index_t MPerBlock = FlatmmConfig::M_Tile;
|
||||
|
||||
ck_tile::index_t sorted_tile_num = num_tokens * topk / MPerBlock;
|
||||
ck_tile::index_t valid_tile_num = sorted_tile_num;
|
||||
ck_tile::index_t sorted_size = sorted_tile_num * MPerBlock;
|
||||
|
||||
const ck_tile::index_t M = sorted_tile_num * MPerBlock;
|
||||
const ck_tile::index_t outputN = kind == ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up ? N / 2 : N;
|
||||
|
||||
static_assert(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::ColumnMajor>);
|
||||
constexpr bool IsInputGemm = kind != ck_tile::MoeFlatmmKind::kFFN_gemm2;
|
||||
|
||||
stride_A = ck_tile::get_default_stride(
|
||||
IsInputGemm ? num_tokens : num_tokens * topk, K, stride_A, is_row_major(a_layout));
|
||||
stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
|
||||
stride_C = ck_tile::get_default_stride(
|
||||
IsInputGemm ? num_tokens * topk : num_tokens, outputN, stride_C, is_row_major(CLayout{}));
|
||||
|
||||
auto a_m_k_tensor = ck_tile::HostTensor<ADataType>(ck_tile::host_tensor_descriptor(
|
||||
IsInputGemm ? num_tokens : num_tokens * topk, K, stride_A, is_row_major(a_layout)));
|
||||
auto b_k_n_tensor = ck_tile::HostTensor<BDataType>(
|
||||
is_row_major(b_layout)
|
||||
? ck_tile::host_tensor_descriptor(experts * N, K, stride_B, is_row_major(b_layout))
|
||||
: ck_tile::host_tensor_descriptor(K, experts * N, stride_B, is_row_major(b_layout)));
|
||||
auto c_m_n_tensor = ck_tile::HostTensor<CDataType>(ck_tile::host_tensor_descriptor(
|
||||
IsInputGemm ? num_tokens * topk : num_tokens, outputN, stride_C, is_row_major(CLayout{})));
|
||||
|
||||
ck_tile::HostTensor<ScaleType> scale_b(ck_tile::HostTensorDescriptor(
|
||||
{K * experts / ScaleGranularityK, N / ScaleGranularityN}, {N / ScaleGranularityN, 1}));
|
||||
|
||||
if(init_method == 0)
|
||||
{
|
||||
ck_tile::FillUniformDistribution<ADataType>{0.0f, 1.0f}(a_m_k_tensor);
|
||||
ck_tile::FillUniformDistribution<BDataType>{-.5f, .5f}(b_k_n_tensor);
|
||||
ck_tile::FillUniformDistribution<ScaleType>{0.f, 1.f}(scale_b);
|
||||
}
|
||||
else
|
||||
{
|
||||
ck_tile::FillUniformDistribution<ADataType>{1.0f, 1.0f}(a_m_k_tensor);
|
||||
ck_tile::FillUniformDistribution<BDataType>{1.0f, 1.0f}(b_k_n_tensor);
|
||||
ck_tile::FillUniformDistribution<ScaleType>{1.0f, 1.0f}(scale_b);
|
||||
}
|
||||
|
||||
ck_tile::HostTensor<BDataType> b_shuffle_host(
|
||||
ck_tile::host_tensor_descriptor(K, experts * N, stride_B, is_row_major(b_layout)));
|
||||
shuffle_mxfp4_weight<FlatmmConfig, kind>(
|
||||
b_k_n_tensor.begin(), b_shuffle_host.begin(), experts, N, K);
|
||||
|
||||
ck_tile::HostTensor<ScaleType> scale_b_shuffle =
|
||||
shuffle_mxfp4_scale<FlatmmConfig, kind>(scale_b, experts);
|
||||
ck_tile::DeviceMem scale_b_shuffle_dev_buf(scale_b_shuffle.get_element_space_size_in_bytes());
|
||||
|
||||
std::cout << "moe_flatmm:"
|
||||
<< "\n num_experts: " << experts << "\n num_tokens: " << num_tokens
|
||||
<< "\n topk: " << topk << "\n sorted_tile_num: " << sorted_tile_num
|
||||
<< "\n problem_n: " << N << "\n problem_k: " << K
|
||||
<< "\n a_m_k: " << a_m_k_tensor.mDesc << "\n b_k_n: " << b_k_n_tensor.mDesc
|
||||
<< "\n b_shuffle: " << b_shuffle_host.mDesc << "\n c_m_n: " << c_m_n_tensor.mDesc
|
||||
<< std::endl;
|
||||
|
||||
ck_tile::HostTensor<ck_tile::index_t> expert_ids(
|
||||
ck_tile::HostTensorDescriptor({sorted_tile_num}, {1}));
|
||||
ck_tile::HostTensor<ck_tile::index_t> sorted_token_ids(
|
||||
ck_tile::HostTensorDescriptor({sorted_size}, {1}));
|
||||
ck_tile::HostTensor<AccDataType> expert_weight(
|
||||
ck_tile::HostTensorDescriptor({sorted_size}, {1}));
|
||||
ck_tile::HostTensor<ck_tile::index_t> max_token_id(
|
||||
ck_tile::HostTensorDescriptor({1 + sorted_tile_num}));
|
||||
|
||||
if(init_method == 0)
|
||||
{
|
||||
// for verification only, no need to satify weight normalization
|
||||
ck_tile::FillUniformDistribution<AccDataType>{0.0f, 1.0f}(expert_weight);
|
||||
}
|
||||
else
|
||||
{
|
||||
ck_tile::FillUniformDistribution<AccDataType>{1.0f, 1.0f}(expert_weight);
|
||||
}
|
||||
|
||||
max_token_id.mData = {valid_tile_num * MPerBlock, 0, 1, 2, 3, 4, 6, 7, 8, 8};
|
||||
// int eids[] = {0, 1, 2, 3, 4, 4, 5, 6, 3, 3, 3, 3}; // {2, 1, 1, 2, 2, 2, 1, 2}
|
||||
|
||||
for(int i = 0; i < sorted_tile_num; i++)
|
||||
{
|
||||
expert_ids.mData[i] = i / ((valid_tile_num + experts - 1) / experts);
|
||||
}
|
||||
|
||||
int token_per_tile = (num_tokens * topk + valid_tile_num - 1) / valid_tile_num;
|
||||
// int token_per_tile = num_tokens * topk / valid_tile_num;
|
||||
int tokenid = 0;
|
||||
// sorted_token_ids.mData[0] = 0;
|
||||
for(int i = 0; i < sorted_tile_num * MPerBlock; i++)
|
||||
{
|
||||
int tile_off = i % MPerBlock;
|
||||
if(tile_off < token_per_tile && tokenid < num_tokens * topk)
|
||||
{
|
||||
sorted_token_ids.mData[i] = (tokenid % num_tokens) | ((tokenid / num_tokens) << 24);
|
||||
tokenid++;
|
||||
}
|
||||
else
|
||||
{
|
||||
sorted_token_ids.mData[i] = num_tokens;
|
||||
}
|
||||
}
|
||||
|
||||
ck_tile::DeviceMem a_m_k_dev_buf{a_m_k_tensor.get_element_space_size_in_bytes()};
|
||||
ck_tile::DeviceMem b_origin_dev_buf{b_k_n_tensor.get_element_space_size_in_bytes()};
|
||||
ck_tile::DeviceMem b_shuffle_dev_buf{b_shuffle_host.get_element_space_size_in_bytes()};
|
||||
ck_tile::DeviceMem c_m_n_dev_buf{c_m_n_tensor.get_element_space_size_in_bytes()};
|
||||
|
||||
a_m_k_dev_buf.ToDevice(a_m_k_tensor.data());
|
||||
b_origin_dev_buf.ToDevice(b_k_n_tensor.data());
|
||||
b_shuffle_dev_buf.ToDevice(b_shuffle_host.data());
|
||||
c_m_n_dev_buf.SetZero();
|
||||
c_m_n_tensor.SetZero();
|
||||
|
||||
ck_tile::DeviceMem sorted_token_ids_dev{sizeof(ck_tile::index_t) *
|
||||
sorted_token_ids.get_element_space_size_in_bytes()};
|
||||
ck_tile::DeviceMem expert_ids_dev{sizeof(ck_tile::index_t) *
|
||||
expert_ids.get_element_space_size_in_bytes()};
|
||||
ck_tile::DeviceMem max_token_id_dev{sizeof(ck_tile::index_t) *
|
||||
max_token_id.get_element_space_size_in_bytes()};
|
||||
ck_tile::DeviceMem expert_weight_dev{sizeof(AccDataType) *
|
||||
expert_weight.get_element_space_size_in_bytes()};
|
||||
|
||||
sorted_token_ids_dev.ToDevice(sorted_token_ids.data());
|
||||
expert_ids_dev.ToDevice(expert_ids.data());
|
||||
max_token_id_dev.ToDevice(max_token_id.data());
|
||||
expert_weight_dev.ToDevice(expert_weight.data());
|
||||
scale_b_shuffle_dev_buf.ToDevice(scale_b_shuffle.data());
|
||||
|
||||
const ck_tile::index_t* p_sorted_token_ids_dev =
|
||||
static_cast<ck_tile::index_t*>(sorted_token_ids_dev.GetDeviceBuffer());
|
||||
const ck_tile::index_t* p_expert_ids_dev =
|
||||
static_cast<ck_tile::index_t*>(expert_ids_dev.GetDeviceBuffer());
|
||||
const ck_tile::index_t* p_max_token_id_dev =
|
||||
static_cast<ck_tile::index_t*>(max_token_id_dev.GetDeviceBuffer());
|
||||
const AccDataType* p_sorted_expert_weight_dev =
|
||||
static_cast<AccDataType*>(expert_weight_dev.GetDeviceBuffer());
|
||||
auto scale_b_shuffle_dev_ptr =
|
||||
ck_tile::FlatmmScalePointer<ScaleGranularityN, ScaleGranularityK>{
|
||||
static_cast<float*>(scale_b_shuffle_dev_buf.GetDeviceBuffer()), N / ScaleGranularityN};
|
||||
|
||||
using MoeFlatmmArgs = ck_tile::MoeFlatmmHostArgs<
|
||||
ck_tile::FlatmmScalePointer<-1>,
|
||||
ck_tile::FlatmmScalePointer<ScaleGranularityN, ScaleGranularityK>>;
|
||||
MoeFlatmmArgs gemm_desc{p_sorted_token_ids_dev,
|
||||
p_sorted_expert_weight_dev,
|
||||
p_expert_ids_dev,
|
||||
p_max_token_id_dev,
|
||||
a_m_k_dev_buf.GetDeviceBuffer(),
|
||||
b_shuffle_dev_buf.GetDeviceBuffer(),
|
||||
c_m_n_dev_buf.GetDeviceBuffer(),
|
||||
num_tokens,
|
||||
experts,
|
||||
topk,
|
||||
1, // k_batch
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
stride_A,
|
||||
stride_B,
|
||||
stride_C,
|
||||
nullptr,
|
||||
scale_b_shuffle_dev_ptr};
|
||||
|
||||
invoke_a16w4_moe_gemm<FlatmmConfig,
|
||||
ADataType,
|
||||
BDataType,
|
||||
ck_tile::tuple<>,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
ck_tile::tuple<>,
|
||||
CLayout,
|
||||
kind>(warmup, repeat, gemm_desc);
|
||||
|
||||
c_m_n_dev_buf.FromDevice(c_m_n_tensor.data());
|
||||
|
||||
bool pass{true};
|
||||
if(arg_parser.get_int("validate"))
|
||||
{
|
||||
ck_tile::HostTensor<CDataType> c_m_n_host_ref(
|
||||
ck_tile::host_tensor_descriptor(IsInputGemm ? num_tokens * topk : num_tokens,
|
||||
outputN,
|
||||
stride_C,
|
||||
is_row_major(CLayout{})));
|
||||
c_m_n_host_ref.SetZero();
|
||||
|
||||
ck_tile::HostTensor<AccDataType> scale_A(
|
||||
ck_tile::HostTensorDescriptor({1, K / ScaleGranularityK}, {1, 1}));
|
||||
|
||||
// scaleA = 1 has no effect on the result
|
||||
ck_tile::FillUniformDistribution<AccDataType>{1.f, 1.f}(scale_A);
|
||||
ck_tile::DeviceMem scale_A_dev_buf(scale_A.get_element_space_size_in_bytes());
|
||||
scale_A_dev_buf.ToDevice(scale_A.data());
|
||||
|
||||
// convert scale_b from e8m0 to float
|
||||
ck_tile::HostTensor<AccDataType> scale_b_float(ck_tile::HostTensorDescriptor(
|
||||
{K * experts / ScaleGranularityK, N / ScaleGranularityN}, {N / ScaleGranularityN, 1}));
|
||||
std::copy(scale_b.begin(), scale_b.end(), scale_b_float.begin());
|
||||
ck_tile::DeviceMem scale_b_float_dev_buf(scale_b_float.get_element_space_size_in_bytes());
|
||||
scale_b_float_dev_buf.ToDevice(scale_b_float.data());
|
||||
|
||||
std::unique_ptr<ck_tile::DeviceMem> c_m_n_ref_buf =
|
||||
std::make_unique<ck_tile::DeviceMem>(c_m_n_tensor.get_element_space_size_in_bytes());
|
||||
c_m_n_ref_buf->SetZero();
|
||||
|
||||
ck_tile::reference_moe_gemm_gpu<
|
||||
ADataType,
|
||||
BDataType,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
CLayout,
|
||||
static_cast<int>(kind),
|
||||
std::conditional_t<IsInputGemm, LocalSilu, ck_tile::identity>>(
|
||||
p_sorted_token_ids_dev,
|
||||
p_expert_ids_dev,
|
||||
p_max_token_id_dev,
|
||||
static_cast<const ADataType*>(a_m_k_dev_buf.GetDeviceBuffer()),
|
||||
static_cast<const BDataType*>(b_origin_dev_buf.GetDeviceBuffer()),
|
||||
static_cast<CDataType*>(c_m_n_ref_buf->GetDeviceBuffer()),
|
||||
p_sorted_expert_weight_dev,
|
||||
num_tokens,
|
||||
MPerBlock,
|
||||
topk,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
stride_A,
|
||||
stride_B,
|
||||
stride_C,
|
||||
M,
|
||||
1,
|
||||
ScaleGranularityK,
|
||||
static_cast<float*>(scale_A_dev_buf.GetDeviceBuffer()),
|
||||
static_cast<float*>(scale_b_float_dev_buf.GetDeviceBuffer()));
|
||||
|
||||
const float max_accumulated_value =
|
||||
*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
|
||||
c_m_n_ref_buf->FromDevice(c_m_n_host_ref.data());
|
||||
|
||||
const float rtol = std::is_same_v<ADataType, ck_tile::half_t> && IsInputGemm ? 1e-3 : 1e-2;
|
||||
const float atol = std::is_same_v<ADataType, ck_tile::half_t> && IsInputGemm ? 1e-3 : 1e-2;
|
||||
|
||||
pass = ck_tile::check_err(
|
||||
c_m_n_tensor, c_m_n_host_ref, "Error: Incorrect results!", rtol, atol);
|
||||
|
||||
std::cout << "Relative error threshold: " << rtol << " Absolute error threshold: " << atol
|
||||
<< std::endl;
|
||||
std::cout << "The CPU verification result is:" << (pass ? "correct" : "fail") << std::endl;
|
||||
}
|
||||
|
||||
return pass;
|
||||
}
|
||||
@@ -165,6 +165,10 @@ float moe_gemm(const ck_tile::MoeFlatmmHostArgs<ScaleM, ScaleN>& args,
|
||||
has_hot_loop_v,
|
||||
tail_number_v>;
|
||||
|
||||
constexpr int BlockedXDLN_PerWarp = moe_kind == ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up
|
||||
? 2
|
||||
: 1; // determined by scale shuffle pattern
|
||||
|
||||
using GemmEpilogue = ck_tile::CShuffleEpilogue<
|
||||
ck_tile::CShuffleEpilogueProblem<ADataType,
|
||||
BDataType,
|
||||
@@ -187,7 +191,8 @@ float moe_gemm(const ck_tile::MoeFlatmmHostArgs<ScaleM, ScaleN>& args,
|
||||
FlatmmConfig::NumWaveGroups,
|
||||
false,
|
||||
1,
|
||||
FlatmmConfig::TiledMMAPermuteN>>;
|
||||
FlatmmConfig::TiledMMAPermuteN,
|
||||
BlockedXDLN_PerWarp>>;
|
||||
|
||||
using CodegenFlatmmPipeline =
|
||||
ck_tile::MoeFlatmmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem>;
|
||||
|
||||
@@ -170,7 +170,6 @@ int run_moe_gemm_example_with_layouts(int argc,
|
||||
ck_tile::HostTensor<AccDataType> per_channel_scale(
|
||||
ck_tile::HostTensorDescriptor({N * experts}, {1}));
|
||||
|
||||
ck_tile::FillMonotonicSeq<AccDataType>{}(per_token_scale);
|
||||
ck_tile::FillUniformDistribution<AccDataType>{0.f, 1.f}(per_token_scale);
|
||||
ck_tile::FillUniformDistribution<AccDataType>{0.f, 1.f}(per_channel_scale);
|
||||
|
||||
@@ -195,12 +194,12 @@ int run_moe_gemm_example_with_layouts(int argc,
|
||||
|
||||
for(int i = 0; i < sorted_tile_num; i++)
|
||||
{
|
||||
expert_ids.mData[i] = i / (valid_tile_num / experts);
|
||||
expert_ids.mData[i] = i / ((valid_tile_num + experts - 1) / experts);
|
||||
}
|
||||
|
||||
int token_per_tile = (num_tokens * topk + valid_tile_num - 1) / valid_tile_num;
|
||||
// int token_per_tile = num_tokens * topk / valid_tile_num;
|
||||
int tokenid = 0;
|
||||
int tokenid = 0;
|
||||
// sorted_token_ids.mData[0] = 0;
|
||||
for(int i = 0; i < sorted_tile_num * MPerBlock; i++)
|
||||
{
|
||||
@@ -329,8 +328,8 @@ int run_moe_gemm_example_with_layouts(int argc,
|
||||
K, 1 /*kbatch*/, max_accumulated_value);
|
||||
c_m_n_ref_buf->FromDevice(c_m_n_host_ref.data());
|
||||
|
||||
float rtol = 1e-3;
|
||||
float atol = 1e-3;
|
||||
const float rtol = std::is_same_v<ADataType, ck_tile::half_t> && IsInputGemm ? 1e-3 : 1e-2;
|
||||
const float atol = std::is_same_v<ADataType, ck_tile::half_t> && IsInputGemm ? 1e-3 : 1e-2;
|
||||
|
||||
pass = ck_tile::check_err(
|
||||
c_m_n_tensor, c_m_n_host_ref, "Error: Incorrect results!", rtol, atol);
|
||||
|
||||
Reference in New Issue
Block a user