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Multiple fixes to GroupedGemm+SplitK (#707)

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* Add license header.

* Reduce number of logged output. Add constant initialization.

* Add functional tests for grouped_gemm with different kbatch value.

* Add debug log informations + remove unused code.

* Don't pass kbatch to CalculateKPadded.

* Turn on logging in grouped gemm and gemm splitk profiler

* Debug: limit number of test cases to run;

* Log more information and initialize with constant value.

* Turn on DEBUG_LOG

* Add more debug log informations.

* Limit the number of instances to compile.

* Use GridwiseGemmPipeline

* Use KBatch to calculate K0

* Multiple DebugLog messages.

* Unit tests for multiple KBatch values.

* Refactoring

* Disable logging
* extract out of if statement KBatch update.

* Uncomment instances.

* Disable DebugLog.

* Use Kbatch when calculate KPadded.

* Fix CGridDesc padding.

* Use available helper functions.

* Uncomment code commented for debuggin.

* Remove unnecessary debug log messages.

* Uncomment previously commented code for debug purposes.

* Add KBatch info to profiler output summary log.

* Add gtests for gemm splitk using ckProfiler API.

* Add more test-cases for different data layout.

* Add more test cases for gemm splitk

* Remove old test.

* Unit tests for MKNK ggemm interface.

* Fix and add more unit-tests.

* Constepxr everything!

* Increase error threshold for fp16 and splitk.

Since we're using fp16 atomic add for splitk there's a
known precision loss.

---------

Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

[ROCm/composable_kernel commit: 70e4eb567f]
This commit is contained in:
Adam Osewski
2023-05-30 14:09:06 +02:00
committed by GitHub
parent 18002ddb3c
commit b145984ea1
20 changed files with 1263 additions and 471 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
include/ck/tensor_operation/gpu/device/device_grouped_gemm.hpp
View File

@@ -1,4 +1,8 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iostream>
#include <vector>

14
include/ck/tensor_operation/gpu/device/impl/device_gemm_xdl_splitk_c_shuffle.hpp
View File

@@ -73,6 +73,11 @@ struct DeviceGemmXdlSplitKCShuffle : public DeviceGemmSplitK<ALayout,
static constexpr auto I2 = Number<2>{};
static constexpr auto I3 = Number<3>{};
// TODO: should be exposed as Tparams.
static constexpr index_t NumGemmKPrefetchStage = 1;
static constexpr LoopScheduler LoopSched = make_default_loop_scheduler();
static constexpr PipelineVersion PipelineVer = PipelineVersion::v2;
using GridwiseGemm = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
BlockSize,
ADataType, // TODO: distinguish A/B datatype
@@ -85,6 +90,7 @@ struct DeviceGemmXdlSplitKCShuffle : public DeviceGemmSplitK<ALayout,
BElementwiseOperation,
CElementwiseOperation,
GemmSpec,
NumGemmKPrefetchStage,
MPerBlock,
NPerBlock,
K0PerBlock,
@@ -112,7 +118,9 @@ struct DeviceGemmXdlSplitKCShuffle : public DeviceGemmSplitK<ALayout,
CShuffleMRepeatPerShuffle,
CShuffleNRepeatPerShuffle,
CBlockTransferScalarPerVector_NWaveNPerXDL,
CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
LoopSched,
PipelineVer>;
using Argument = typename GridwiseGemm::Argument;
using DefaultBlock2CTileMap = typename GridwiseGemm::DefaultBlock2CTileMap;
@@ -257,7 +265,7 @@ struct DeviceGemmXdlSplitKCShuffle : public DeviceGemmSplitK<ALayout,
StrideC,
GridwiseGemm::CalculateMPadded(M),
GridwiseGemm::CalculateNPadded(N),
GridwiseGemm::CalculateKPadded(K),
GridwiseGemm::CalculateKPadded(K, KBatch),
GridwiseGemm::CalculateK0(K, KBatch),
KBatch};
}
@@ -290,7 +298,7 @@ struct DeviceGemmXdlSplitKCShuffle : public DeviceGemmSplitK<ALayout,
StrideC,
GridwiseGemm::CalculateMPadded(M),
GridwiseGemm::CalculateNPadded(N),
GridwiseGemm::CalculateKPadded(K),
GridwiseGemm::CalculateKPadded(K, KBatch),
GridwiseGemm::CalculateK0(K, KBatch),
KBatch);
}

28
include/ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_splitk_cshuffle.hpp
View File

@@ -85,7 +85,7 @@ template <typename ALayout,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
GemmSpecialization GemmSpec,
ck::index_t NumPrefetch,
ck::index_t NumGemmKPrefetchStage,
ck::index_t BlockSize,
ck::index_t MPerBlock,
ck::index_t NPerBlock,
@@ -152,6 +152,7 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
BElementwiseOperation,
CDEElementwiseOperation,
GemmSpec,
NumGemmKPrefetchStage,
MPerBlock,
NPerBlock,
K0PerBlock,
@@ -179,7 +180,9 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
CShuffleMXdlPerWavePerShuffle,
CShuffleNXdlPerWavePerShuffle,
CDEBlockTransferScalarPerVector_NPerBlock,
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
LoopSched,
PipelineVersion::v2>;
using CGridDesc_M_N = typename GridwiseGemm::CGridDesc_M_N;
using Block2ETileMapKSplit =
@@ -265,8 +268,7 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
const index_t k_padded = GridwiseGemm::CalculateKPadded(K, K_BATCH);
const index_t k0 = GridwiseGemm::CalculateK0(K, K_BATCH);
const auto c_grid_desc_m_n =
GridwiseGemm::MakeCGridDescriptor_M_N(M, N, m_padded, n_padded, stride_c);
const auto c_grid_desc_m_n = GridwiseGemm::MakeCGridDescriptor_M_N(M, N, stride_c);
const auto local_b2c_tile_map =
Block2ETileMapKSplit{c_grid_desc_m_n, B2E_M01, K_BATCH};
@@ -319,8 +321,8 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
const index_t k_padded = GridwiseGemm::CalculateKPadded(karg.K, K_BATCH);
const index_t k0 = GridwiseGemm::CalculateK0(karg.K, K_BATCH);
const auto c_grid_desc_m_n = GridwiseGemm::MakeCGridDescriptor_M_N(
karg.M, karg.N, karg.MPadded, karg.NPadded, karg.StrideC);
const auto c_grid_desc_m_n =
GridwiseGemm::MakeCGridDescriptor_M_N(karg.M, karg.N, karg.StrideC);
const auto local_b2c_tile_map =
Block2ETileMapKSplit{c_grid_desc_m_n, B2E_M01, K_BATCH};
@@ -501,6 +503,11 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
if((ck::type_convert<ck::index_t>(arg.gemm_kernel_args_.size()) +
arg.skipped_group_count_) != arg.group_count_)
{
#if DEBUG_LOG
std::cout << "The group count is not equal to sum of skipped groups "
"and kernel args size!"
<< std::endl;
#endif // DEBUG_LOG
return false;
}
@@ -509,14 +516,15 @@ struct DeviceGroupedGemmXdlSplitKCShuffle : public DeviceGroupedGemmSplitK<ALayo
{
const auto& a = arg.gemm_kernel_args_[i].karg_;
bool group_arg_valid = GridwiseGemm::CheckValidity(a);
#if DEBUG_LOG
if(not group_arg_valid)
{
std::cout << "[" << __func__ << "] group id: " << i << " is not supported!\n";
#if DEBUG_LOG
std::cout << "[" << __func__ << "] group id: " << i
<< " has invalid GridwiseGemm settings!" << std::endl;
a.Print();
}
#endif // DEBUG_LOG
supported &= group_arg_valid;
}
supported = supported && group_arg_valid;
}
return supported;
}

180
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r4r2.hpp
View File

@@ -8,14 +8,14 @@
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
namespace ck {
@@ -55,6 +55,7 @@ template <index_t BlockSize,
typename BElementwiseOperation,
typename CElementwiseOperation,
tensor_operation::device::GemmSpecialization GemmSpec,
index_t NumGemmKPrefetchStage,
index_t MPerBlock,
index_t NPerBlock,
index_t K0PerBlock,
@@ -82,7 +83,9 @@ template <index_t BlockSize,
index_t CShuffleMRepeatPerShuffle,
index_t CShuffleNRepeatPerShuffle,
index_t CBlockTransferScalarPerVector_NWaveNPerXDL,
typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>
typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
LoopScheduler LoopSched = make_default_loop_scheduler(),
PipelineVersion PipelineVer = PipelineVersion::v1>
struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
{
static constexpr auto I0 = Number<0>{};
@@ -99,8 +102,15 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
static constexpr auto M01 = 1;
static constexpr auto N01 = 1;
static constexpr auto gemm_padder =
tensor_operation::device::GemmPadder<GemmSpec, index_t, index_t, index_t>{
MPerBlock, NPerBlock, K1* K0PerBlock};
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
using GridwiseGemmPipe = remove_cvref_t<decltype(
GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;
struct Argument : public ck::tensor_operation::device::BaseArgument
{
const FloatAB* p_a_grid;
@@ -176,12 +186,12 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
// prefer this to be called on host
__host__ __device__ static auto CalculateMPadded(index_t M)
{
return (M + MPerBlock - 1) / MPerBlock * MPerBlock;
return math::integer_least_multiple(M, MPerBlock);
}
__host__ __device__ static auto CalculateNPadded(index_t N)
{
return (N + NPerBlock - 1) / NPerBlock * NPerBlock;
return math::integer_least_multiple(N, NPerBlock);
}
__host__ __device__ static auto CalculateK0(index_t K, index_t K_Batch = 1)
@@ -295,8 +305,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
}
}
__host__ __device__ static auto
MakeCGridDescriptor_M_N(index_t M, index_t N, index_t MPad, index_t NPad, index_t StrideC)
__host__ __device__ 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)
@@ -309,22 +318,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
}
}();
if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding)
{
return transform_tensor_descriptor(c_grid_desc_m_n,
make_tuple(make_right_pad_transform(M, MPad - M),
make_right_pad_transform(N, NPad - N)),
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>{}));
}
return gemm_padder.PadCDescriptor_M_N(c_grid_desc_m_n);
}
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
@@ -383,7 +377,15 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
{
if(!(karg.M % MPerBlock == 0))
{
#if DEBUG_LOG
std::cout << "Arg M value is not a multiple of MPerBlock! M: " << karg.M << " "
<< __FILE__ << ":" << __LINE__ << ", in function: " << __func__
<< std::endl;
#endif // DEBUG_LOG
return false;
}
}
if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||
GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
@@ -391,40 +393,116 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
{
if(!(karg.N % NPerBlock == 0))
{
#if DEBUG_LOG
std::cout << "Arg N value is not a multiple of NPerBlock! N: " << karg.N << " "
<< __FILE__ << ":" << __LINE__ << ", in function: " << __func__
<< std::endl;
#endif // DEBUG_LOG
return false;
}
}
if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
{
if(karg.K % ABlockTransferSrcScalarPerVector != 0)
{
#if DEBUG_LOG
std::cout << "Arg K (" << karg.K
<< ") value is not a multiple of ABlockTransferSrcScalarPerVector ("
<< ABlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
<< __LINE__ << ", in function: " << __func__ << std::endl;
#endif // DEBUG_LOG
return false;
}
}
else
{
if(karg.M % ABlockTransferSrcScalarPerVector != 0)
{
#if DEBUG_LOG
std::cout << "Arg M (" << karg.M
<< ") value is not a multiple of ABlockTransferSrcScalarPerVector ("
<< ABlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
<< __LINE__ << ", in function: " << __func__ << std::endl;
#endif // DEBUG_LOG
return false;
}
}
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
{
if(karg.N % BBlockTransferSrcScalarPerVector != 0)
{
#if DEBUG_LOG
std::cout << "Arg N (" << karg.N
<< ") value is not a multiple of BBlockTransferSrcScalarPerVector ("
<< BBlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
<< __LINE__ << ", in function: " << __func__ << std::endl;
#endif // DEBUG_LOG
return false;
}
}
else
{
if(karg.K % BBlockTransferSrcScalarPerVector != 0)
{
#if DEBUG_LOG
std::cout << "Arg K (" << karg.K
<< ") value is not a multiple of BBlockTransferSrcScalarPerVector ("
<< BBlockTransferSrcScalarPerVector << " )! " << __FILE__ << ":"
<< __LINE__ << ", in function: " << __func__ << std::endl;
#endif // DEBUG_LOG
return false;
}
}
if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
{
if(karg.N % CBlockTransferScalarPerVector_NWaveNPerXDL != 0)
{
#if DEBUG_LOG
std::cout
<< "Arg N (" << karg.N
<< ") value is not a multiple of CBlockTransferScalarPerVector_NWaveNPerXDL ("
<< CBlockTransferScalarPerVector_NWaveNPerXDL << " )! " << __FILE__ << ":"
<< __LINE__ << ", in function: " << __func__ << std::endl;
#endif // DEBUG_LOG
return false;
}
}
else
{
if(karg.M % CBlockTransferScalarPerVector_NWaveNPerXDL != 0)
{
#if DEBUG_LOG
std::cout
<< "Arg M (" << karg.M
<< ") value is not a multiple of CBlockTransferScalarPerVector_NWaveNPerXDL ("
<< CBlockTransferScalarPerVector_NWaveNPerXDL << " )! " << __FILE__ << ":"
<< __LINE__ << ", in function: " << __func__ << std::endl;
#endif // DEBUG_LOG
return false;
}
}
const auto num_k_loop = karg.K0 / K0PerBlock;
if(!GridwiseGemmPipe::IsSupported(num_k_loop))
{
#if DEBUG_LOG
std::cout << "The number of k loops (" << num_k_loop
<< ") value is not supported by GridwiseGemm Pipeline."
<< " K0: " << karg.K0 << ", K0PerBlock: " << K0PerBlock << " " << __FILE__
<< ":" << __LINE__ << ", in function: " << __func__ << std::endl;
#endif // DEBUG_LOG
return false;
}
return true;
@@ -439,9 +517,8 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
__host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
{
const bool has_main_k0_block_loop = K0 > K0PerBlock;
return has_main_k0_block_loop;
const index_t num_loop = K0 / K0PerBlock;
return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
}
template <typename CGridDesc>
@@ -490,7 +567,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
return BlockToCTileMap_3DGrid_KSplit<MPerBlock, NPerBlock>();
}
using CGridDesc_M_N = remove_cvref_t<decltype(MakeCGridDescriptor_M_N(1, 1, 1, 1, 1))>;
using CGridDesc_M_N = remove_cvref_t<decltype(MakeCGridDescriptor_M_N(1, 1, 1))>;
using DefaultBlock2CTileMap = remove_cvref_t<decltype(MakeDefaultBlock2CTileMap())>;
template <bool HasMainKBlockLoop,
@@ -507,8 +584,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
karg.M, karg.MPadded, karg.K, karg.StrideA, karg.k_batch, karg.K0, karg.KPadded);
const auto b_b_k0_n_k1_grid_desc = MakeBGridDescriptor_KBatch_K0_N_K1(
karg.K, karg.NPadded, karg.N, karg.StrideB, karg.k_batch, karg.K0, karg.KPadded);
const auto c_grid_desc_m_n =
MakeCGridDescriptor_M_N(karg.M, karg.N, karg.MPadded, karg.NPadded, karg.StrideC);
const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N(karg.M, karg.N, karg.StrideC);
const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n);
@@ -680,20 +756,8 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
// sanity check
#if 1
auto blockwise_gemm =
BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
FloatAB,
FloatAcc,
decltype(a_k0_m_k1_block_desc),
decltype(b_k0_n_k1_block_desc),
MPerXDL,
NPerXDL,
MRepeat,
NRepeat,
K1>{};
#else
auto blockwise_gemm = BlockwiseGemmXdlopsInterwave_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
FloatAB,
FloatAcc,
@@ -703,9 +767,8 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
NPerXDL,
MRepeat,
NRepeat,
K1>{};
#endif
K1,
LoopSched>();
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
@@ -761,7 +824,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
k0_block_data_begin += K0PerBlock;
} while(k0_block_data_begin < (K0 - K0PerBlock));
} while(k0_block_data_begin < (karg.K0 - K0PerBlock));
}
// tail
@@ -772,13 +835,12 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
}
#else
// gridwise GEMM pipeline
const auto gridwise_gemm_pipeline =
GridwiseGemmPipeline_Selector<PipelineVersion::v2, 1, LoopScheduler::Default>();
const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
(a_b_k0_m_k1_grid_desc.GetLength(I1) * a_b_k0_m_k1_grid_desc.GetLength(I3)) /
(K0PerBlock * K1));
const auto gridwise_gemm_pipeline = GridwiseGemmPipe{};
gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(a_b_k0_m_k1_grid_desc,
a_b_k0_m_k1_block_desc,
a_blockwise_copy,
@@ -993,24 +1055,6 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
}
}
template <typename Layout>
struct LStr
{
static std::string Get() { return ""; }
};
template <>
struct LStr<ck::tensor_layout::gemm::RowMajor>
{
static std::string Get() { return "R"; }
};
template <>
struct LStr<ck::tensor_layout::gemm::ColumnMajor>
{
static std::string Get() { return "C"; }
};
static std::string GetTypeString()
{
auto str = std::stringstream();

1
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instance.cpp
View File

@@ -64,6 +64,7 @@ using device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_irregular_tile_instances = st
//###################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//###################| | | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//###################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemm_Xdl< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 2, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemm_Xdl< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,

6
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_irregular_instance.cpp
View File

@@ -44,14 +44,14 @@ using device_grouped_gemm_xdl_splitk_f16_f16_f16_mk_kn_mn_irregular_tile_instanc
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 192, 64, 32, 8, 8, 32, 32, 3, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 64, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 48, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 64, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 48, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
// DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 24, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 24, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 192, 32, 32, 8, 8, 32, 32, 3, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 64, 32, 8, 8, 32, 32, 1, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Row, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 64, 32, 32, 8, 8, 32, 32, 1, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,

4
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_irregular_instance.cpp
View File

@@ -37,7 +37,7 @@ using device_grouped_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_irregular_tile_instanc
//################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 192, 64, 32, 8, 8, 32, 32, 3, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
// DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 64, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 48, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 64, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 48, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
@@ -45,7 +45,7 @@ using device_grouped_gemm_xdl_splitk_f16_f16_f16_mk_nk_mn_irregular_tile_instanc
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 192, 32, 32, 8, 8, 32, 32, 3, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
// DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 192, 32, 8, 8, 32, 32, 1, 3, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdlSplitKCShuffle< Row, Col, Empty_Tuple, Row, F16, F16, F32, F16, Empty_Tuple, F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding, 1, 128, 32, 256, 32, 8, 8, 32, 32, 1, 4, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,

6
profiler/include/profiler/profile_gemm_splitk_impl.hpp
View File

@@ -246,9 +246,9 @@ bool profile_gemm_splitk_impl(int do_verification,
}
std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
<< " StrideB = " << StrideB << " StrideC = " << StrideC << " : " << best_ave_time
<< " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
<< best_op_name << std::endl;
<< " StrideB = " << StrideB << " StrideC = " << StrideC << " KBatch = " << KBatch
<< " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
<< " GB/s, " << best_op_name << std::endl;
return pass;
}

120
profiler/include/profiler/profile_grouped_gemm_impl.hpp
View File

@@ -19,6 +19,7 @@
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/utility/fill.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
namespace ck {
@@ -43,7 +44,6 @@ bool profile_grouped_gemm_impl(int do_verification,
const std::vector<int>& StrideCs,
int kbatch = 1)
{
bool pass = true;
auto f_host_tensor_descriptor =
@@ -81,11 +81,11 @@ bool profile_grouped_gemm_impl(int do_verification,
c_m_n_device_results.push_back(
Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
#if DEBUG_LOG
std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n[" << i
<< "]:" << b_k_n[i].mDesc << ", c_m_n_device_results[" << i
<< "]:" << c_m_n_device_results[i].mDesc << std::endl;
#endif // DEBUG_LOG
std::size_t num_thread = 1;
switch(init_method)
{
@@ -191,65 +191,71 @@ bool profile_grouped_gemm_impl(int do_verification,
DeviceMem gemm_desc_workspace(gemm_ptr->GetWorkSpaceSize(argument_ptr.get()));
gemm_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_desc_workspace.GetDeviceBuffer());
std::string gemm_name = gemm_ptr->GetTypeString();
if(kbatch > 1)
{
using DeviceOpSplitK =
ck::tensor_operation::device::DeviceGroupedGemmSplitK<ALayout,
BLayout,
ck::Tuple<>,
CLayout,
ADataType,
BDataType,
ck::Tuple<>,
CDataType,
AElementOp,
BElementOp,
CElementOp>;
if(dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get()) != nullptr)
{
dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get())
->SetKBatchSize(argument_ptr.get(), kbatch);
}
}
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{
std::string gemm_name = gemm_ptr->GetTypeString();
if(kbatch > 1)
{
using DeviceOpSplitK =
ck::tensor_operation::device::DeviceGroupedGemmSplitK<ALayout,
BLayout,
ck::Tuple<>,
CLayout,
ADataType,
BDataType,
ck::Tuple<>,
CDataType,
AElementOp,
BElementOp,
CElementOp>;
if(dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get()) != nullptr)
{
dynamic_cast<DeviceOpSplitK*>(gemm_ptr.get())
->SetKBatchSize(argument_ptr.get(), kbatch);
}
}
float ave_time =
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = 0, num_btype = 0;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
if(time_kernel)
{
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
std::size_t flop = 0, num_btype = 0;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
num_btype += sizeof(ADataType) * Ms[i] * Ks[i] + sizeof(BDataType) * Ks[i] * Ns[i] +
sizeof(CDataType) * Ms[i] * Ns[i];
}
num_btype += sizeof(ADataType) * Ms[i] * Ks[i] +
sizeof(BDataType) * Ks[i] * Ns[i] +
sizeof(CDataType) * Ms[i] * Ns[i];
}
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
<< gb_per_sec << " GB/s, " << gemm_name << std::endl;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
<< " TFlops, " << gb_per_sec << " GB/s, " << gemm_name << std::endl;
if(tflops > best_tflops)
{
best_gemm_name = gemm_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
if(tflops > best_tflops)
{
best_gemm_name = gemm_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
if(do_verification)
{
bool instance_pass = true;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());
c_device_buf[i]->SetZero();
Tensor<CDataType> c_m_n_host_result(
f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{}));
@@ -274,7 +280,20 @@ bool profile_grouped_gemm_impl(int do_verification,
c_element_op);
ref_invoker.Run(ref_argument);
pass = pass && ck::utils::check_err(c_m_n_device_results[i], c_m_n_host_result);
if(std::is_same_v<CDataType, ck::half_t> && kbatch > 1)
{
instance_pass =
instance_pass && ck::utils::check_err(c_m_n_device_results[i],
c_m_n_host_result,
"Error: Incorrect results!",
0.06);
}
else
{
instance_pass =
instance_pass &&
ck::utils::check_err(c_m_n_device_results[i], c_m_n_host_result);
}
if(do_log)
{
@@ -289,16 +308,25 @@ bool profile_grouped_gemm_impl(int do_verification,
<< std::endl;
}
}
std::cout << "Instance: " << gemm_name << " verification "
<< (instance_pass ? "SUCCEED" : "FAILED") << std::endl;
pass = pass && instance_pass;
}
}
else
{
std::cout << "does not support this GEMM problem" << std::endl;
std::cout << "Instance: " << gemm_name << ", does not support this GEMM problem"
<< std::endl;
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
if(time_kernel)
{
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
}
return pass;
}

5
test/gemm_split_k/CMakeLists.txt
View File

@@ -1,5 +1,4 @@
if(GPU_TARGETS MATCHES "gfx908" OR GPU_TARGETS MATCHES "gfx90a" OR GPU_TARGETS MATCHES "gfx940")
add_test_executable(test_gemm_split_k gemm_split_k.cpp)
target_link_libraries(test_gemm_split_k PRIVATE utility)
target_link_libraries(test_gemm_split_k PRIVATE device_gemm_splitk_instance)
add_gtest_executable(test_gemm_splitk test_gemm_splitk.cpp)
target_link_libraries(test_gemm_splitk PRIVATE utility device_gemm_splitk_instance)
endif()

261
test/gemm_split_k/gemm_split_k.cpp
View File

@@ -1,261 +0,0 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_splitk.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/host_gemm.hpp"
enum struct GemmMatrixLayout
{
MK_KN_MN, // 0
MK_NK_MN, // 1
KM_KN_MN, // 2
KM_NK_MN, // 3
};
template <typename T>
static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
{
float max_diff = 1e-6;
for(std::size_t i = 0; i < ref.mData.size(); ++i)
{
float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
if(max_diff < diff)
{
return false;
}
}
return true;
}
struct gemmArgs
{
GemmMatrixLayout layout;
int M;
int N;
int K;
int StrideA;
int StrideB;
int StrideC;
int KBatch;
};
int test_gemm(const gemmArgs& args)
{
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
bool a_row_major, b_row_major, c_row_major;
switch(args.layout)
{
case GemmMatrixLayout::MK_KN_MN:
a_row_major = true;
b_row_major = true;
c_row_major = true;
break;
case GemmMatrixLayout::MK_NK_MN:
a_row_major = true;
b_row_major = false;
c_row_major = true;
break;
case GemmMatrixLayout::KM_KN_MN:
a_row_major = false;
b_row_major = true;
c_row_major = true;
break;
case GemmMatrixLayout::KM_NK_MN:
a_row_major = false;
b_row_major = false;
c_row_major = true;
break;
default: printf("not supported layout"); return 1;
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, bool row_major) {
using namespace ck::literals;
if(row_major)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<float> a_m_k(f_host_tensor_descriptor(args.M, args.K, args.StrideA, a_row_major));
Tensor<float> b_k_n(f_host_tensor_descriptor(args.K, args.N, args.StrideB, b_row_major));
Tensor<float> c_m_n_host_result(
f_host_tensor_descriptor(args.M, args.N, args.StrideC, c_row_major));
Tensor<float> c_m_n_device_result(
f_host_tensor_descriptor(args.M, args.N, args.StrideC, c_row_major));
// init data
std::size_t num_thread = 1;
a_m_k.GenerateTensorValue(GeneratorTensor_2<float>{-5, 5}, num_thread);
b_k_n.GenerateTensorValue(GeneratorTensor_2<float>{-5, 5}, num_thread);
// set zero to c_device_buf
c_m_n_device_result.GenerateTensorValue(GeneratorTensor_0<float>{}, num_thread);
host_gemm_mk_kn_mn(a_m_k,
b_k_n,
c_m_n_host_result,
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{});
DeviceMem a_device_buf(sizeof(float) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(float) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_device_buf(sizeof(float) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data());
c_device_buf.ToDevice(c_m_n_device_result.mData.data());
auto test = [&](auto a_layout, auto b_layout, auto c_layout) {
bool success = false;
using DeviceOp = ck::tensor_operation::device::DeviceGemmSplitK<decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
float,
float,
float,
PassThrough,
PassThrough,
PassThrough>;
const auto gemm_ptrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
for(auto& gemm_ptr : gemm_ptrs)
{
auto argument_ptr =
gemm_ptr->MakeArgumentPointer(static_cast<float*>(a_device_buf.GetDeviceBuffer()),
static_cast<float*>(b_device_buf.GetDeviceBuffer()),
static_cast<float*>(c_device_buf.GetDeviceBuffer()),
args.M,
args.N,
args.K,
args.StrideA,
args.StrideB,
args.StrideC,
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
args.KBatch);
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get());
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
if(!check_out(c_m_n_host_result, c_m_n_device_result))
{
success = false;
break;
}
success = true;
}
}
return success;
};
bool success = false;
if(args.layout == GemmMatrixLayout::MK_KN_MN)
{
success = test(Row{}, Row{}, Row{});
}
else if(args.layout == GemmMatrixLayout::MK_NK_MN)
{
success = test(Row{}, Col{}, Row{});
}
else if(args.layout == GemmMatrixLayout::KM_KN_MN)
{
success = test(Col{}, Row{}, Row{});
}
else
{
success = test(Col{}, Col{}, Row{});
}
auto error_code = 0;
if(success)
{
std::cout << "test split k : Pass" << std::endl;
}
else
{
std::cout << "test split k: Fail " << std::endl;
error_code = -1; // test needs to report failure
}
return error_code;
}
int main(int argc, char* argv[])
{
std::vector<gemmArgs> test_cases;
if(argc == 1)
{
test_cases = {{GemmMatrixLayout::MK_KN_MN, 1024, 1024, 1024, 1024, 1024, 1024, 2},
{GemmMatrixLayout::MK_KN_MN, 1024, 1024, 1024, 1024, 1024, 1024, 8}};
}
else if(argc == 9)
{
const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[1]));
const int M = std::stoi(argv[2]);
const int N = std::stoi(argv[3]);
const int K = std::stoi(argv[4]);
const int StrideA = std::stoi(argv[5]);
const int StrideB = std::stoi(argv[6]);
const int StrideC = std::stoi(argv[7]);
const int KBatch = std::stoi(argv[8]);
test_cases = {{layout, M, N, K, StrideA, StrideB, StrideC, KBatch}};
}
else
{
printf("arg1: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
printf(" 3: A[k, m] * B[n, k] = C[m, n])\n");
printf("arg2 to 7: M, N, K, StrideA, StrideB, StrideC KBatch\n");
return -1;
}
bool error = false;
for(const auto& kinder : test_cases)
{
error |= test_gemm(kinder);
}
return error ? 1 : 0;
}

66
test/gemm_split_k/test_gemm_splitk.cpp Normal file
View File

@@ -0,0 +1,66 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <tuple>
#include "gtest/gtest.h"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "test_gemm_splitk_util.hpp"
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
namespace {
template <typename X, typename Y>
struct tuple_concat;
template <typename... Xs, typename... Ys>
struct tuple_concat<std::tuple<Xs...>, std::tuple<Ys...>>
{
using type = std::tuple<Xs..., Ys...>;
};
} // namespace
template <typename Tuple>
class TestGemmSplitK_MK_KN
: public ck::test::TestGemmSplitK<typename tuple_concat<std::tuple<Row, Row>, Tuple>::type>
{
};
template <typename Tuple>
class TestGemmSplitK_MK_NK
: public ck::test::TestGemmSplitK<typename tuple_concat<std::tuple<Row, Col>, Tuple>::type>
{
};
template <typename Tuple>
class TestGemmSplitK_KM_KN
: public ck::test::TestGemmSplitK<typename tuple_concat<std::tuple<Col, Row>, Tuple>::type>
{
};
template <typename Tuple>
class TestGemmSplitK_KM_NK
: public ck::test::TestGemmSplitK<typename tuple_concat<std::tuple<Col, Col>, Tuple>::type>
{
};
// clang-format off
using KernelTypes = ::testing::Types<
// ADataType, BDataType, CDataType
std::tuple< F16, F16, F16>,
std::tuple< F32, F32, F32>
>;
// clang-format on
TYPED_TEST_SUITE(TestGemmSplitK_MK_KN, KernelTypes);
TYPED_TEST_SUITE(TestGemmSplitK_MK_NK, KernelTypes);
TYPED_TEST_SUITE(TestGemmSplitK_KM_KN, KernelTypes);
TYPED_TEST_SUITE(TestGemmSplitK_KM_NK, KernelTypes);
#include "test_gemm_splitk_ut_cases.inc"

217
test/gemm_split_k/test_gemm_splitk_ut_cases.inc Normal file
View File

@@ -0,0 +1,217 @@
#pragma once
TYPED_TEST(TestGemmSplitK_MK_KN, SmallM)
{
std::vector<int> Ms{0, 1, 2, 3, 4, 5, 6};
constexpr int N = 512;
constexpr int K = 320;
constexpr int StrideA = K;
constexpr int StrideB = N;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_MK_NK, SmallM)
{
std::vector<int> Ms{0, 1, 2, 3, 4, 5, 6};
constexpr int N = 512;
constexpr int K = 320;
constexpr int StrideA = K;
constexpr int StrideB = K;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_KM_KN, SmallM)
{
std::vector<int> Ms{0, 1, 2, 3, 4, 5, 6};
constexpr int N = 512;
constexpr int K = 320;
constexpr int StrideB = N;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, M, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_KM_NK, SmallM)
{
std::vector<int> Ms{0, 1, 2, 3, 4, 5, 6};
constexpr int N = 512;
constexpr int K = 320;
constexpr int StrideB = K;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, M, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_MK_KN, MidLargeM)
{
std::vector<int> Ms{127, 255, 312, 799, 1573};
constexpr int N = 512;
constexpr int K = 320;
constexpr int StrideA = K;
constexpr int StrideB = N;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_MK_NK, MidLargeM)
{
std::vector<int> Ms{127, 255, 312, 799, 1573};
constexpr int N = 512;
constexpr int K = 320;
constexpr int StrideA = K;
constexpr int StrideB = K;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_KM_KN, MidLargeM)
{
std::vector<int> Ms{127, 255, 312, 799, 1573};
constexpr int N = 512;
constexpr int K = 320;
constexpr int StrideB = N;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, M, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_KM_NK, MidLargeM)
{
std::vector<int> Ms{127, 255, 312, 799, 1573};
constexpr int N = 512;
constexpr int K = 320;
constexpr int StrideB = K;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, M, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_MK_KN, PaddK)
{
std::vector<int> Ms{127};
constexpr int N = 512;
constexpr int K = 437;
constexpr int StrideA = K;
constexpr int StrideB = N;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_MK_NK, PaddK)
{
std::vector<int> Ms{127};
constexpr int N = 512;
constexpr int K = 437;
constexpr int StrideA = K;
constexpr int StrideB = K;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_KM_KN, PaddK)
{
std::vector<int> Ms{127};
constexpr int N = 512;
constexpr int K = 437;
constexpr int StrideB = N;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, M, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_KM_NK, PaddK)
{
std::vector<int> Ms{127};
constexpr int N = 512;
constexpr int K = 437;
constexpr int StrideB = K;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, M, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_MK_KN, Regular)
{
std::vector<int> Ms{512};
constexpr int N = 512;
constexpr int K = 512;
constexpr int StrideA = K;
constexpr int StrideB = N;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_MK_NK, Regular)
{
std::vector<int> Ms{512};
constexpr int N = 512;
constexpr int K = 512;
constexpr int StrideA = K;
constexpr int StrideB = K;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_KM_KN, Regular)
{
std::vector<int> Ms{512};
constexpr int N = 512;
constexpr int K = 512;
constexpr int StrideB = N;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, M, StrideB, StrideC);
}
TYPED_TEST(TestGemmSplitK_KM_NK, Regular)
{
std::vector<int> Ms{512};
constexpr int N = 512;
constexpr int K = 512;
constexpr int StrideB = K;
constexpr int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, M, StrideB, StrideC);
}

78
test/gemm_split_k/test_gemm_splitk_util.hpp Normal file
View File

@@ -0,0 +1,78 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
#include <sstream>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "include/ck/utility/data_type.hpp"
#include "profiler/profile_gemm_splitk_impl.hpp"
namespace ck {
namespace test {
template <typename Tuple>
class TestGemmSplitK : public testing::Test
{
using Row = ck::tensor_layout::gemm::RowMajor;
using F32 = float;
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CLayout = Row;
using ADataType = std::tuple_element_t<2, Tuple>;
using BDataType = std::tuple_element_t<3, Tuple>;
using CDataType = std::tuple_element_t<4, Tuple>;
public:
static constexpr bool verify_ = true;
static constexpr int init_method_ = 1; // decimal value initialization
static constexpr bool log_ = false;
static constexpr bool bench_ = false; // measure kernel performance
std::vector<int> k_batches_;
void SetUp() override { k_batches_ = {1, 2, 3, 5, 8}; }
void Run(const int M,
const int N,
const int K,
const int StrideA,
const int StrideB,
const int StrideC)
{
for(auto kb : k_batches_)
{
RunSingle(M, N, K, StrideA, StrideB, StrideC, kb);
}
}
void RunSingle(const int M,
const int N,
const int K,
const int StrideA,
const int StrideB,
const int StrideC,
int kbatch = 1)
{
bool pass = ck::profiler::profile_gemm_splitk_impl<ADataType,
BDataType,
F32,
CDataType,
ALayout,
BLayout,
CLayout>(
verify_, init_method_, log_, bench_, M, N, K, StrideA, StrideB, StrideC, kbatch);
EXPECT_TRUE(pass);
}
};
} // namespace test
} // namespace ck

10
test/grouped_gemm/CMakeLists.txt
View File

@@ -1,5 +1,9 @@
if(GPU_TARGETS MATCHES "gfx908" OR GPU_TARGETS MATCHES "gfx90a" OR GPU_TARGETS MATCHES "gfx940")
add_test_executable(test_grouped_gemm_fp16 grouped_gemm_fp16.cpp)
target_link_libraries(test_grouped_gemm_fp16 PRIVATE utility)
target_link_libraries(test_grouped_gemm_fp16 PRIVATE device_grouped_gemm_instance)
add_custom_target(test_grouped_gemm)
add_gtest_executable(test_grouped_gemm_splitk test_grouped_gemm_splitk.cpp)
add_gtest_executable(test_grouped_gemm_interface test_grouped_gemm_interface.cpp)
target_link_libraries(test_grouped_gemm_splitk PRIVATE utility device_grouped_gemm_instance)
target_link_libraries(test_grouped_gemm_interface PRIVATE utility device_grouped_gemm_instance)
add_dependencies(test_grouped_gemm test_grouped_gemm_splitk test_grouped_gemm_interface)
endif()

69
test/grouped_gemm/grouped_gemm_fp16.cpp
View File

@@ -1,69 +0,0 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <random>
#include "profiler/profile_grouped_gemm_impl.hpp"
namespace {
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using CDataType = ck::half_t;
using AccDataType = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
template <typename ALayout, typename BLayout, typename CLayout>
bool TestGroupedGemm()
{
std::mt19937 gen(19391);
std::uniform_int_distribution<> distrib(1, 10);
int group_count = distrib(gen);
// GEMM shape
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
std::vector<const void*> p_a, p_b;
std::vector<void*> p_c;
std::vector<int> Ms, Ns, Ks, StrideAs, StrideBs, StrideCs;
for(int i = 0; i < group_count; i++)
{
Ms.push_back(256 + 256 * distrib(gen));
Ns.push_back(256 + 256 * distrib(gen));
Ks.push_back(128 + 128 * distrib(gen));
StrideAs.push_back(std::is_same<Row, ALayout>::value ? Ks[i] : Ms[i]);
StrideBs.push_back(std::is_same<Row, BLayout>::value ? Ns[i] : Ks[i]);
StrideCs.push_back(std::is_same<Row, CLayout>::value ? Ns[i] : Ms[i]);
}
return ck::profiler::profile_grouped_gemm_impl<ADataType,
BDataType,
CDataType,
AccDataType,
ALayout,
BLayout,
CLayout>(
true, 1, false, 1, Ms, Ns, Ks, StrideAs, StrideBs, StrideCs);
}
} // anonymous namespace
int main()
{
bool res = true;
res = res && TestGroupedGemm<Row, Row, Row>();
res = res && TestGroupedGemm<Row, Col, Row>();
res = res && TestGroupedGemm<Col, Row, Row>();
res = res && TestGroupedGemm<Col, Col, Row>();
std::cout << "TestGroupedGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
return res ? 0 : 1;
}

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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "test_grouped_gemm_util.hpp"
class TestGGemmSplitKInterface_MKNKMN : public ::testing::Test
{
protected:
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using ALayout = Row;
using BLayout = Col;
using ELayout = Row;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
template <ck::tensor_operation::device::GemmSpecialization GemmSpec,
ck::index_t KPerBlock,
ck::index_t K1,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t CDEBlockTransferScalarPerVector_NPerBlock>
using GGemmInstance =
ck::test::DeviceGroupedGemmSplitkInstanceWrapper<ALayout,
BLayout,
ELayout,
GemmSpec,
KPerBlock,
K1,
ABlockTransferSrcScalarPerVector,
BBlockTransferSrcScalarPerVector,
CDEBlockTransferScalarPerVector_NPerBlock>;
using DefaultGGemmInstance = GGemmInstance<GemmDefault, 32, 8, 4, 8, 8>;
};
TEST_F(TestGGemmSplitKInterface_MKNKMN, TileSize)
{
std::vector<int> Ms{128, 256, 188, 512};
constexpr int N = 256;
constexpr int K = 128;
std::vector<int> Ns(Ms.size(), N);
std::vector<int> Ks(Ms.size(), K);
std::vector<int> StrideAs(Ms.size(), K);
std::vector<int> StrideBs(Ms.size(), K);
std::vector<int> StrideCs(Ms.size(), N);
// M % MPerBlock
EXPECT_FALSE(DefaultGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
Ms = std::vector<int>{256, 128, 128, 512};
Ns = std::vector<int>{256, 177, 128, 512};
// N % NPerBlock
EXPECT_FALSE(DefaultGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
}
TEST_F(TestGGemmSplitKInterface_MKNKMN, VectorLoadWidth)
{
static constexpr auto GemmMNKPadding =
ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using PaddedGGemmInstance = GGemmInstance<GemmMNKPadding, 32, 8, 4, 8, 8>;
std::vector<int> Ms{128, 256, 256, 512};
constexpr int N = 256;
constexpr int K = 512;
std::vector<int> Ns(Ms.size(), N);
std::vector<int> Ks(Ms.size(), K);
std::vector<int> StrideAs(Ms.size(), K);
std::vector<int> StrideBs(Ms.size(), K);
std::vector<int> StrideCs(Ms.size(), N);
// K % ABlockTransferSrcScalarPerVector
Ks = std::vector<int>{256, 177, 128, 512};
EXPECT_FALSE(PaddedGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
Ks = std::vector<int>{256, 164, 128, 512};
// K % BBlockTransferSrcScalarPerVector
EXPECT_FALSE(PaddedGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
Ks = std::vector<int>(4, 128);
Ns = std::vector<int>{256, 127, 128, 512};
// N % CBlockTransferScalarPerVector_NWaveNPerXDL
EXPECT_FALSE(PaddedGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
}
TEST_F(TestGGemmSplitKInterface_MKNKMN, KLoops)
{
std::vector<int> Ms{128, 256, 256, 512};
constexpr int N = 256;
constexpr int K = 128;
constexpr int kbatch = 4;
std::vector<int> Ns(Ms.size(), N);
std::vector<int> Ks(Ms.size(), K);
std::vector<int> StrideAs(Ms.size(), K);
std::vector<int> StrideBs(Ms.size(), K);
std::vector<int> StrideCs(Ms.size(), N);
// kloops % 2
Ks = std::vector<int>{256, 512, 320, 768};
EXPECT_FALSE(
DefaultGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, kbatch));
// Not all gemms have same value for main_k0_block_loop!
Ks = std::vector<int>{256, 512, 512, 512};
EXPECT_THROW(DefaultGGemmInstance{}.Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, kbatch),
std::runtime_error);
}
class TestGGemmSplitKInterface_KMKNNM : public ::testing::Test
{
protected:
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using ALayout = Col;
using BLayout = Row;
using ELayout = Col;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
template <ck::tensor_operation::device::GemmSpecialization GemmSpec,
ck::index_t KPerBlock,
ck::index_t K1,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferSrcScalarPerVector,
ck::index_t CDEBlockTransferScalarPerVector_NPerBlock>
using GGemmInstance =
ck::test::DeviceGroupedGemmSplitkInstanceWrapper<ALayout,
BLayout,
ELayout,
GemmSpec,
KPerBlock,
K1,
ABlockTransferSrcScalarPerVector,
BBlockTransferSrcScalarPerVector,
CDEBlockTransferScalarPerVector_NPerBlock>;
using DefaultGGemmInstance = GGemmInstance<GemmDefault, 32, 8, 4, 8, 4>;
};
TEST_F(TestGGemmSplitKInterface_KMKNNM, TileSize)
{
std::vector<int> Ms{128, 256, 188, 512};
constexpr int N = 256;
constexpr int K = 128;
std::vector<int> Ns(Ms.size(), N);
std::vector<int> Ks(Ms.size(), K);
std::vector<int> StrideAs(Ms.size(), K);
std::vector<int> StrideBs(Ms.size(), K);
std::vector<int> StrideCs(Ms.size(), N);
// M % MPerBlock
EXPECT_FALSE(DefaultGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
Ms = std::vector<int>{128, 256, 256, 512};
Ns = std::vector<int>{256, 177, 128, 512};
// N % NPerBlock
EXPECT_FALSE(DefaultGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
}
TEST_F(TestGGemmSplitKInterface_KMKNNM, VectorLoadWidth)
{
static constexpr auto GemmMNKPadding =
ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using PaddedGGemmInstance = GGemmInstance<GemmMNKPadding, 32, 8, 2, 8, 4>;
std::vector<int> Ms{128, 256, 256, 512};
constexpr int N = 256;
constexpr int K = 512;
std::vector<int> Ns(Ms.size(), N);
std::vector<int> Ks(Ms.size(), K);
std::vector<int> StrideAs(Ms.size(), K);
std::vector<int> StrideBs(Ms.size(), K);
std::vector<int> StrideCs(Ms.size(), N);
// M % ABlockTransferSrcScalarPerVector
Ms = std::vector<int>{256, 177, 128, 512};
EXPECT_FALSE(PaddedGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
Ms = std::vector<int>{128, 256, 256, 512};
Ns = std::vector<int>{256, 164, 128, 512};
// N % BBlockTransferSrcScalarPerVector
EXPECT_FALSE(PaddedGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
Ns = std::vector<int>{128, 256, 256, 512};
Ms = std::vector<int>{256, 130, 128, 512};
// M % CBlockTransferScalarPerVector_NWaveNPerXDL
EXPECT_FALSE(PaddedGGemmInstance{}.IsSupported(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs));
}

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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <tuple>
#include <vector>
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/utility/data_type.hpp"
#include "gtest/gtest.h"
#include "test_grouped_gemm_util.hpp"
using F16 = ck::half_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using RRR_F16_F16_F16 = ck::test::TestGroupedGemm<std::tuple<Row, Row, Row, F16, F16, F16>>;
using RCR_F16_F16_F16 = ck::test::TestGroupedGemm<std::tuple<Row, Col, Row, F16, F16, F16>>;
using RRR_F16_F16_F16_LargeK = ck::test::TestGroupedGemm<std::tuple<Row, Row, Row, F16, F16, F16>>;
using RCR_F16_F16_F16_LargeK = ck::test::TestGroupedGemm<std::tuple<Row, Col, Row, F16, F16, F16>>;
const std::vector<int> KBATCH{1, 2, 3, 5, 8};
INSTANTIATE_TEST_SUITE_P(TestGroupedGemm_splitk_MK_KN, RRR_F16_F16_F16, testing::ValuesIn(KBATCH));
INSTANTIATE_TEST_SUITE_P(TestGroupedGemm_splitk_MK_NK, RCR_F16_F16_F16, testing::ValuesIn(KBATCH));
INSTANTIATE_TEST_SUITE_P(TestGroupedGemm_splitk_LargeK_MK_KN,
RRR_F16_F16_F16_LargeK,
testing::Values(32, 64));
INSTANTIATE_TEST_SUITE_P(TestGroupedGemm_splitk_LargeK_MK_NK,
RCR_F16_F16_F16_LargeK,
testing::Values(32, 64));
#include "test_grouped_gemm_ut_cases.inc"

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#pragma once
TEST_P(RRR_F16_F16_F16, TinyCases)
{
const std::vector<int> Ms{0, 1};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), N);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RRR_F16_F16_F16, SmallCases)
{
const std::vector<int> Ms{2, 1, 3, 4, 5, 0};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), N);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RRR_F16_F16_F16, MidCases)
{
const std::vector<int> Ms{167, 183, 177, 153, 139, 204};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), N);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RRR_F16_F16_F16, Regular)
{
const std::vector<int> Ms{64, 128, 256};
constexpr int N = 768;
constexpr int K = 320;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), N);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RRR_F16_F16_F16, MNKPadded)
{
const std::vector<int> Ms{127, 150, 188, 210};
constexpr int N = 136;
constexpr int K = 280;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), N);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RCR_F16_F16_F16, TinyCases)
{
const std::vector<int> Ms{0, 1};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), K);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RCR_F16_F16_F16, SmallCases)
{
const std::vector<int> Ms{2, 1, 3, 4, 5, 0};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), K);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RCR_F16_F16_F16, MidCases)
{
const std::vector<int> Ms{167, 183, 177, 153, 139, 204};
constexpr int N = 768;
constexpr int K = 544;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), K);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RCR_F16_F16_F16, Regular)
{
const std::vector<int> Ms{32, 64, 128, 256};
constexpr int N = 768;
constexpr int K = 320;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), K);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RCR_F16_F16_F16, MNKPadded)
{
const std::vector<int> Ms{127, 150, 188, 210};
constexpr int N = 136;
constexpr int K = 280;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), K);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RRR_F16_F16_F16_LargeK, TestLargeKBatch)
{
const std::vector<int> Ms{188, 210};
constexpr int N = 768;
constexpr int K = 4096;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), N);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}
TEST_P(RCR_F16_F16_F16_LargeK, TestLargeKBatch)
{
const std::vector<int> Ms{188, 210};
constexpr int N = 768;
constexpr int K = 4096;
const std::vector<int> Ns(Ms.size(), N);
const std::vector<int> Ks(Ms.size(), K);
const std::vector<int> StrideAs(Ms.size(), K);
const std::vector<int> StrideBs(Ms.size(), K);
const std::vector<int> StrideCs(Ms.size(), N);
this->Run(Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, this->GetParam());
}

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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <array>
#include <string>
#include <sstream>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/stream_config.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_splitk_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/utility/data_type.hpp"
#include "ck/utility/sequence.hpp"
#include "ck/utility/tuple.hpp"
#include "ck/utility/number.hpp"
#include "profiler/profile_grouped_gemm_impl.hpp"
namespace ck {
namespace test {
template <typename Range>
std::string serialize_range(const Range& range)
{
std::stringstream ss;
for(auto& r : range)
{
ss << r << ", ";
}
std::string str = ss.str();
return std::string(str.begin(), str.end() - 2);
}
template <typename Tuple>
class TestGroupedGemm : public testing::TestWithParam<int>
{
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using ELayout = std::tuple_element_t<2, Tuple>;
using ADataType = std::tuple_element_t<3, Tuple>;
using BDataType = std::tuple_element_t<4, Tuple>;
using EDataType = std::tuple_element_t<5, Tuple>;
public:
static constexpr bool verify_ = true;
static constexpr int init_method_ = 1; // decimal value initialization
static constexpr bool log_ = false;
static constexpr bool bench_ = false; // measure kernel performance
void SetUp() override {}
void Run(const std::vector<int>& Ms,
const std::vector<int>& Ns,
const std::vector<int>& Ks,
const std::vector<int>& StrideAs,
const std::vector<int>& StrideBs,
const std::vector<int>& StrideCs,
int kbatch = 1)
{
bool pass = ck::profiler::profile_grouped_gemm_impl<ADataType,
BDataType,
EDataType,
float,
ALayout,
BLayout,
ELayout>(
verify_, init_method_, log_, bench_, Ms, Ns, Ks, StrideAs, StrideBs, StrideCs, kbatch);
EXPECT_TRUE(pass);
}
};
template <typename ALayout,
typename BLayout,
typename ELayout,
tensor_operation::device::GemmSpecialization GemmSpec,
ck::index_t KPerBlock,
ck::index_t K1,
ck::index_t ABlockTransferSrcScalarPerVector,
ck::index_t BBlockTransferSrcScalarPerVector,
index_t CDEBlockTransferScalarPerVector_NPerBlock>
struct DeviceGroupedGemmSplitkInstanceWrapper
{
using F16 = half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = tensor_operation::element_wise::PassThrough;
using EmptyTuple = ck::Tuple<>;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
template <ck::index_t N>
using I = ck::Number<N>;
using ABlockTransferThreadClusterArrageOrder =
std::conditional_t<std::is_same_v<ALayout, Row>, S<0, 2, 1, 3>, S<0, 1, 3, 2>>;
using ABlockTransferSrcAccessOrder =
std::conditional_t<std::is_same_v<ALayout, Row>, S<0, 2, 1, 3>, S<0, 1, 3, 2>>;
using ABlockTransferSrcVectorDim = std::conditional_t<std::is_same_v<ALayout, Row>, I<3>, I<2>>;
using ABlockTransferDstScalarPerVector_K1 =
std::conditional_t<std::is_same_v<ALayout, Row>, I<8>, I<2>>;
using ABlockLdsAddExtraM = std::conditional_t<std::is_same_v<ALayout, Row>, I<1>, I<0>>;
using BBlockTransferThreadClusterArrageOrder =
std::conditional_t<std::is_same_v<BLayout, Row>, S<0, 1, 3, 2>, S<0, 2, 1, 3>>;
using BBlockTransferSrcAccessOrder =
std::conditional_t<std::is_same_v<BLayout, Row>, S<0, 1, 3, 2>, S<0, 2, 1, 3>>;
using BBlockTransferSrcVectorDim = std::conditional_t<std::is_same_v<BLayout, Row>, I<2>, I<3>>;
using BBlockTransferDstScalarPerVector_K1 =
std::conditional_t<std::is_same_v<ALayout, Row>, I<2>, I<8>>;
using BBlockLdsAddExtraM = std::conditional_t<std::is_same_v<ALayout, Row>, I<0>, I<1>>;
using DeviceGroupedGemmSplitKInstance =
tensor_operation::device::DeviceGroupedGemmXdlSplitKCShuffle<
ALayout,
BLayout,
EmptyTuple,
ELayout,
F16,
F16,
F32,
F16,
EmptyTuple,
F16,
PassThrough,
PassThrough,
PassThrough,
GemmSpec,
1,
128,
128,
128,
KPerBlock,
K1,
K1,
32,
32,
4,
2,
S<1, 4, 32, 1>,
ABlockTransferThreadClusterArrageOrder,
ABlockTransferSrcAccessOrder,
ABlockTransferSrcVectorDim::value,
ABlockTransferSrcScalarPerVector,
ABlockTransferDstScalarPerVector_K1::value,
ABlockLdsAddExtraM::value,
S<1, 4, 32, 1>,
BBlockTransferThreadClusterArrageOrder,
BBlockTransferSrcAccessOrder,
BBlockTransferSrcVectorDim::value,
BBlockTransferSrcScalarPerVector,
BBlockTransferDstScalarPerVector_K1::value,
BBlockLdsAddExtraM::value,
1,
1,
S<1, 16, 1, 8>,
CDEBlockTransferScalarPerVector_NPerBlock>;
bool IsSupported(const std::vector<int>& Ms,
const std::vector<int>& Ns,
const std::vector<int>& Ks,
const std::vector<int>& StrideAs,
const std::vector<int>& StrideBs,
const std::vector<int>& StrideCs,
int kbatch = 1) const
{
std::size_t n_groups = Ms.size();
EXPECT_TRUE(Ns.size() == n_groups && Ks.size() == n_groups && StrideAs.size() == n_groups &&
StrideBs.size() == n_groups && StrideCs.size() == n_groups)
<< "The number of groups is not consistent!";
std::vector<tensor_operation::device::GemmDesc> gemm_descs;
for(std::size_t i = 0; i < n_groups; ++i)
{
gemm_descs.push_back(tensor_operation::device::GemmDesc{
Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i], {}});
}
std::vector<const void*> p_As(n_groups, nullptr);
std::vector<const void*> p_Bs(n_groups, nullptr);
std::vector<void*> p_Cs(n_groups, nullptr);
auto p_Ds = std::vector<std::array<const void*, 0>>{};
auto ggemm_instance = DeviceGroupedGemmSplitKInstance{};
auto argument = ggemm_instance.MakeArgument(
p_As, p_Bs, p_Ds, p_Cs, gemm_descs, PassThrough{}, PassThrough{}, PassThrough{});
if(kbatch > 1)
{
ggemm_instance.SetKBatchSize(argument, kbatch);
}
return ggemm_instance.IsSupportedArgument(argument);
}
float Run(const std::vector<int>& Ms,
const std::vector<int>& Ns,
const std::vector<int>& Ks,
const std::vector<int>& StrideAs,
const std::vector<int>& StrideBs,
const std::vector<int>& StrideCs,
int kbatch = 1) const
{
std::size_t n_groups = Ms.size();
EXPECT_TRUE(Ns.size() == n_groups && Ks.size() == n_groups && StrideAs.size() == n_groups &&
StrideBs.size() == n_groups && StrideCs.size() == n_groups)
<< "The number of groups is not consistent!";
std::vector<tensor_operation::device::GemmDesc> gemm_descs;
for(std::size_t i = 0; i < n_groups; ++i)
{
gemm_descs.push_back(tensor_operation::device::GemmDesc{
Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i], {}});
}
std::vector<const void*> p_As(n_groups, nullptr);
std::vector<const void*> p_Bs(n_groups, nullptr);
std::vector<void*> p_Cs(n_groups, nullptr);
auto p_Ds = std::vector<std::array<const void*, 0>>{};
auto ggemm_instance = DeviceGroupedGemmSplitKInstance{};
auto argument = ggemm_instance.MakeArgument(
p_As, p_Bs, p_Ds, p_Cs, gemm_descs, PassThrough{}, PassThrough{}, PassThrough{});
if(kbatch > 1)
{
ggemm_instance.SetKBatchSize(argument, kbatch);
}
EXPECT_TRUE(ggemm_instance.IsSupportedArgument(argument));
auto invoker = ggemm_instance.MakeInvoker();
DeviceMem gemm_desc_workspace(ggemm_instance.GetWorkSpaceSize(&argument));
ggemm_instance.SetWorkSpacePointer(&argument, gemm_desc_workspace.GetDeviceBuffer());
return invoker.Run(argument, StreamConfig{nullptr, false});
}
};
} // namespace test
} // namespace ck