mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-03-21 23:57:39 +00:00
* initial poc * factor out common parts in operator() * cv4 * rest of the universal gemm pipelines * fix test * remove boilerplate from tile engine * fix example * fix example * format * fix tests build for gemm * remove base pipeline codegen from gemm instance builder * unify v3 logic with the rest of universal gemm pipelines * fix build for multi abd test * fix test gemm multi d * fix build for weight preshuffle * fix grouped gemm test * fix grouped gemm multi d test * fix grouped gemm preshuffle * fix grouped gemm example except for quant * fix gemm preshuffle * fix splitk 2 stage example * fix batched gemm example * fix multid example * fix multiabd example * fix batched gemm test * fixup * fix examples build * fix grouped gemm test build * fix smoke builder * hacky poc * fix tile engine * kill the lambda * maybe fix test build * more fixes * clang-format * save temp * clang-format * mostly fix examples * clang-format * remove dead code * more cleanup * fix fmha bwd build (default epilogue set/add appears to be broken) * fix default epilogue tests but not correctness * clang-format * fix bquant * clang-format * cleanup dead code * rearrange make windows for readability * restore changes to IsSupportedArgument * fix smoke-builder * clang-format * fixup rename class * build fixes * clang-format * fix builder * fixup * remove set from builder tests * fix test * clang-format * re-refactor the kernels * clang-format * fix header license * remove memory operation from conv bwd test * clang-format * clang-format example,include * clang-format test * build fixes * clang-format * solve compilation error * fix the CI * solve compilation error * clang format * solve merge conflict * solve merge conflict * solve the gfx11 error * solve test error * moar build fixes * remove AtomicAddRequiresKBatchGreaterThanOne test since the property is removed from the kernel scope --------- Co-authored-by: Thomas Ning <Thomas.Ning@amd.com>
448 lines
19 KiB
C++
448 lines
19 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 <memory>
|
|
|
|
#include "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>>{};
|
|
}
|
|
|
|
template <typename FlatmmConfig, typename T>
|
|
auto flatmm_shuffle_b(const ck_tile::HostTensor<T>& t)
|
|
{
|
|
assert(t.get_lengths().size() == 2);
|
|
int n_ = t.get_lengths()[1];
|
|
int k_ = t.get_lengths()[0];
|
|
|
|
constexpr int MaxVecSize = 16 / sizeof(T);
|
|
constexpr int KLane = ck_tile::get_warp_size() / FlatmmConfig::N_Warp_Tile;
|
|
constexpr int ItemsPerAccess = std::min(MaxVecSize, FlatmmConfig::K_Warp_Tile / KLane);
|
|
|
|
ck_tile::HostTensor<T> t_view({n_ / FlatmmConfig::N_Warp_Tile,
|
|
FlatmmConfig::N_Warp_Tile,
|
|
k_ / ItemsPerAccess,
|
|
ItemsPerAccess});
|
|
std::copy(t.begin(), t.end(), t_view.begin());
|
|
return ck_tile::reference_permute(t_view, {0, 2, 1, 3});
|
|
}
|
|
|
|
template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
|
|
auto calculate_rtol_atol(const ck_tile::index_t K,
|
|
const ck_tile::index_t kbatch,
|
|
const float max_accumulated_value)
|
|
{
|
|
using ComputeType =
|
|
std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
|
|
// Calculate thresholds
|
|
const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
|
|
ck_tile::integer_divide_ceil(K, kbatch));
|
|
const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
|
|
max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
|
|
// Calculate error due to split_k accumulation
|
|
const auto rtol_split_k =
|
|
ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
|
|
const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
|
|
max_accumulated_value, kbatch);
|
|
// Use higher threshold
|
|
return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
|
|
}
|
|
|
|
// 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 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_
|
|
|
|
if constexpr(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 GemmPipelineProblem =
|
|
ck_tile::GemmPipelineProblem<ADataType, BDataType, 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);
|
|
|
|
const auto Run = [&](const auto has_hot_loop_, const auto tail_number_) {
|
|
constexpr bool has_hot_loop_v = has_hot_loop_.value;
|
|
constexpr auto tail_number_v = tail_number_.value;
|
|
constexpr auto scheduler = FlatmmConfig::Scheduler;
|
|
|
|
using CodegenPipelineProblem = ck_tile::FlatmmPipelineProblem<ADataType,
|
|
BDataType,
|
|
AccDataType,
|
|
CodegenFlatmmShape,
|
|
CodegenGemmTraits,
|
|
scheduler,
|
|
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,
|
|
DsDatatype,
|
|
AccDataType,
|
|
CDataType,
|
|
DsLayout,
|
|
ELayout,
|
|
CDEElementWise,
|
|
TilePartitioner::MPerBlock,
|
|
TilePartitioner::NPerBlock,
|
|
FlatmmConfig::M_Warp,
|
|
FlatmmConfig::N_Warp,
|
|
FlatmmConfig::M_Warp_Tile,
|
|
FlatmmConfig::N_Warp_Tile,
|
|
FlatmmConfig::K_Warp_Tile,
|
|
CodegenPipelineProblem::TransposeC,
|
|
FlatmmConfig::NumWaveGroups,
|
|
false,
|
|
1,
|
|
FlatmmConfig::TiledMMAPermuteN,
|
|
BlockedXDLN_PerWarp>>;
|
|
|
|
using CodegenFlatmmPipeline =
|
|
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> ? 2 : 1;
|
|
static constexpr ck_tile::index_t BPackedSize =
|
|
std::is_same_v<BDataType, ck_tile::pk_int4_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_));
|
|
};
|
|
return ck_tile::launch_kernel_time_mask(
|
|
s,
|
|
run_flush_cache,
|
|
ck_tile::make_kernel<FlatmmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
|
|
}
|
|
else
|
|
{
|
|
return ck_tile::launch_kernel(
|
|
s,
|
|
ck_tile::make_kernel<FlatmmConfig::kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
|
|
}
|
|
};
|
|
|
|
float ave_time = BaseGemmPipeline::TailHandler(Run, has_hot_loop, tail_num);
|
|
return ave_time;
|
|
}
|
|
|
|
#include "run_moe_flatmm_example.inc"
|
|
|
|
template <template <typename PreType> typename FlatmmConfig>
|
|
int run_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 prec_type = arg_parser.get_str("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(prec_type == "fp8")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<
|
|
ck_tile::fp8_t,
|
|
FlatmmConfig<ck_tile::fp8_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(prec_type == "bf8")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<
|
|
ck_tile::bf8_t,
|
|
FlatmmConfig<ck_tile::bf8_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(prec_type == "bf16")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<
|
|
ck_tile::bfloat16_t,
|
|
FlatmmConfig<ck_tile::bfloat16_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_up>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(prec_type == "fp16")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<
|
|
ck_tile::half_t,
|
|
FlatmmConfig<ck_tile::half_t>,
|
|
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(prec_type == "fp8")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<
|
|
ck_tile::fp8_t,
|
|
FlatmmConfig<ck_tile::fp8_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_only>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(prec_type == "bf8")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<
|
|
ck_tile::bf8_t,
|
|
FlatmmConfig<ck_tile::bf8_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_only>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(prec_type == "bf16")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<
|
|
ck_tile::bfloat16_t,
|
|
FlatmmConfig<ck_tile::bfloat16_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_only>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(prec_type == "fp16")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<
|
|
ck_tile::half_t,
|
|
FlatmmConfig<ck_tile::half_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm1_gate_only>(argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else
|
|
{
|
|
throw std::runtime_error("Unsupported precision type for gemm1_gate_up!");
|
|
}
|
|
}
|
|
else if(gemm_kind == "gemm2")
|
|
{
|
|
if(prec_type == "fp8")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<ck_tile::fp8_t,
|
|
FlatmmConfig<ck_tile::fp8_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm2>(
|
|
argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(prec_type == "bf8")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<ck_tile::bf8_t,
|
|
FlatmmConfig<ck_tile::bf8_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm2>(
|
|
argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(prec_type == "bf16")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<ck_tile::bfloat16_t,
|
|
FlatmmConfig<ck_tile::bfloat16_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm2>(
|
|
argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else if(prec_type == "fp16")
|
|
{
|
|
return run_moe_gemm_example_with_layouts<ck_tile::half_t,
|
|
FlatmmConfig<ck_tile::half_t>,
|
|
ck_tile::MoeFlatmmKind::kFFN_gemm2>(
|
|
argc, argv, Row{}, Col{}, Row{});
|
|
}
|
|
else
|
|
{
|
|
throw std::runtime_error("Unsupported precision type for gemm1_gate_up!");
|
|
}
|
|
}
|
|
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_moe_flatmm_example<FlatmmConfig16>(argc, argv);
|
|
}
|
|
else if(warp_tile == 1)
|
|
{
|
|
return !run_moe_flatmm_example<FlatmmConfig32>(argc, argv);
|
|
}
|
|
else if(warp_tile == 2)
|
|
{
|
|
return !run_moe_flatmm_example<FlatmmConfig16_950>(argc, argv);
|
|
}
|
|
else
|
|
{
|
|
return !run_moe_flatmm_example<FlatmmConfig32_950>(argc, argv);
|
|
}
|
|
}
|
|
catch(const std::runtime_error& e)
|
|
{
|
|
std::cerr << "Runtime error: " << e.what() << '\n';
|
|
return EXIT_FAILURE;
|
|
}
|
|
}
|