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Unverified Commit b75216fa authored by kylasa's avatar kylasa Committed by GitHub
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Merge branch 'develop' into kylasa_1870

parents 610f9a34 3b230208
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Showing with 292 additions and 137 deletions
CMakeLists.txt
View file @ b75216fa
......@@ -92,6 +92,7 @@ endif()
add_compile_options(-Wno-bit-int-extension)
add_compile_options(-Wno-pass-failed)
add_compile_options(-Wno-switch-default)
add_compile_options(-Wno-unique-object-duplication)
if(DL_KERNELS)
add_definitions(-DDL_KERNELS)
......
example/01_gemm/gemm_xdl_streamk.cpp
View file @ b75216fa
......@@ -27,11 +27,15 @@ using DeviceGemmStreamK = ck::tensor_operation::device::DeviceGemmXdlStreamK
// ######| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise| 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| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
#if defined(CK_USE_AMD_MFMA_GFX950)
< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, 256, 256, 128, 4, 16, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
#else // defined(CK_USE_AMD_MFMA_GFX950)
< ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, 256, 128, 128, 4, 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, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
// < ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, 256, 256, 128, 4, 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, 2, 1, 1, 1, S<1, 32, 1, 8>, 8>;
// < ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, 128, 32, 64, 4, 8, 32, 32, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>;
// < ADataType, BDataType, CDataType, AccDataType, ALayout, BLayout, CLayout, AElementOp, BElementOp, CElementOp, 128, 32, 128, 4, 8, 32, 32, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 2, 1, 1, 1, S<1, 32, 1, 4>, 8>;
#endif // defined(CK_USE_AMD_MFMA_GFX950)
......
example/ck_tile/03_gemm/CMakeLists.txt
View file @ b75216fa
add_executable(tile_example_gemm_basic EXCLUDE_FROM_ALL gemm_basic.cpp)
add_executable(tile_example_gemm_universal EXCLUDE_FROM_ALL universal_gemm.cpp)
target_compile_options(tile_example_gemm_universal PRIVATE
-mllvm -enable-noalias-to-md-conversion=0
)
example/ck_tile/03_gemm/gemm_basic.hpp
View file @ b75216fa
......@@ -11,21 +11,26 @@
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
#define CK_TILE_PIPELINE_COMPUTE 1
#define CK_TILE_PIPELINE_COMPUTE_V3 1
#define CK_TILE_PIPELINE_MEMORY 2
#define CK_TILE_PIPELINE_COMPUTE_V4 3
#ifndef CK_TILE_PIPELINE_DEFAULT
#define CK_TILE_PIPELINE_DEFAULT CK_TILE_PIPELINE_MEMORY
#define CK_TILE_PIPELINE_DEFAULT CK_TILE_PIPELINE_COMPUTE_V3
#endif
#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_MEMORY)
#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrMem
#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrMem
#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Interwave
#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE)
#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V3)
#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrCompV3
#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrCompV3
#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Intrawave
#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V4)
#define GEMM_PIPELINE ck_tile::GemmPipelineAgBgCrCompV4
#define UNIVERSAL_GEMM_PIPELINE ck_tile::BaseGemmPipelineAgBgCrCompV4
#define GEMM_PIPELINE_SCHEDULER ck_tile::GemmPipelineScheduler::Intrawave
#else
#error "unsupported CK_TILE_PIPELINE_DEFAULT value"
#endif
......@@ -126,7 +131,8 @@ auto create_args(int argc, char* argv[])
.insert("warmup", "50", "number of iterations before benchmark the kernel")
.insert("repeat", "100", "number of iterations to benchmark the kernel")
.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
.insert("split_k", "1", "splitK value");
.insert("split_k", "1", "splitK value")
.insert("init", "0", "0:random, 1:linear, 2:constant(1)");
bool result = arg_parser.parse(argc, argv);
return std::make_tuple(result, arg_parser);
......
example/ck_tile/03_gemm/run_gemm_example.inc
View file @ b75216fa
......@@ -107,9 +107,10 @@ int run_gemm_example_with_layouts(int argc,
ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
ck_tile::index_t stride_C = arg_parser.get_int("stride_c");
ck_tile::index_t kbatch = arg_parser.get_int("split_k");
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
ck_tile::index_t kbatch = arg_parser.get_int("split_k");
int n_warmup = arg_parser.get_int("warmup");
int n_repeat = arg_parser.get_int("repeat");
ck_tile::index_t init_method = arg_parser.get_int("init");
stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
......@@ -122,9 +123,26 @@ int run_gemm_example_with_layouts(int argc,
ck_tile::HostTensor<CDataType> c_m_n_dev_result(
ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
// TODO: add different init types
ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n);
if(init_method == 0)
{
ck_tile::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
ck_tile::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
}
else if(init_method == 1)
{
ck_tile::FillMonotonicSeq<ADataType>{}(a_m_k);
ck_tile::FillMonotonicSeq<BDataType>{}(b_k_n);
}
else if(init_method == 2)
{
ck_tile::FillConstant<ADataType>{static_cast<ADataType>(1)}(a_m_k);
ck_tile::FillConstant<BDataType>{static_cast<BDataType>(1)}(b_k_n);
}
else
{
a_m_k.SetZero();
b_k_n.SetZero();
}
ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
......
example/ck_tile/03_gemm/script/benchmark_basic.sh
View file @ b75216fa
......@@ -2,7 +2,6 @@
EXE="$(find . -name tile_example_gemm_basic -type f | head -n 1)"
VALID=1
for b_matrix_layout in "C"; do
for m in "64" "512" "1024" "2048"; do
for n in "512" "1024" "2048"; do
......
example/ck_tile/03_gemm/universal_gemm.cpp
View file @ b75216fa
......@@ -34,8 +34,10 @@ float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config&
constexpr ck_tile::index_t M_Warp_Tile = 32;
constexpr ck_tile::index_t N_Warp_Tile = 32;
constexpr ck_tile::index_t K_Warp_Tile = 8;
constexpr bool DoubleSmemBuffer = false;
#endif
#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE)
#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V3)
// Compute friendly for Intrawave scheduler
constexpr ck_tile::index_t M_Tile = 256;
constexpr ck_tile::index_t N_Tile = 256;
......@@ -48,6 +50,24 @@ float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config&
constexpr ck_tile::index_t M_Warp_Tile = 32;
constexpr ck_tile::index_t N_Warp_Tile = 32;
constexpr ck_tile::index_t K_Warp_Tile = 16;
constexpr bool DoubleSmemBuffer = false;
#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V4)
// Compute friendly for Intrawave scheduler
// Using the ping pong reader in the lds level
constexpr ck_tile::index_t M_Tile = 256;
constexpr ck_tile::index_t N_Tile = 256;
constexpr ck_tile::index_t K_Tile = 32;
constexpr ck_tile::index_t M_Warp = 2;
constexpr ck_tile::index_t N_Warp = 2;
constexpr ck_tile::index_t K_Warp = 1;
constexpr ck_tile::index_t M_Warp_Tile = 32;
constexpr ck_tile::index_t N_Warp_Tile = 32;
constexpr ck_tile::index_t K_Warp_Tile = 16;
constexpr bool DoubleSmemBuffer = true;
#endif
constexpr bool kPadM = false;
......@@ -70,8 +90,14 @@ float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config&
GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
using GemmUniversalTraits = ck_tile::
TileGemmUniversalTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout, TransposeC>;
using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<kPadM,
kPadN,
kPadK,
DoubleSmemBuffer,
ALayout,
BLayout,
CLayout,
TransposeC>;
using GemmPipelineProblem =
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;
......@@ -99,8 +125,7 @@ float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config&
has_hot_loop_v,
tail_number_v>;
using GemmPipeline =
GEMM_PIPELINE<UniversalGemmProblem, ck_tile::UniversalGemmPipelineAgBgCrPolicy>;
using GemmPipeline = GEMM_PIPELINE<UniversalGemmProblem>;
using GemmEpilogue = ck_tile::CShuffleEpilogue<
ck_tile::CShuffleEpilogueProblem<AccDataType,
CDataType,
......@@ -140,7 +165,7 @@ float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config&
if(has_hot_loop)
{
#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE)
#if(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V3)
if(tail_num == ck_tile::TailNumber::Full)
{
Run(ck_tile::bool_constant<true>{},
......@@ -215,24 +240,26 @@ float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config&
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Seven>{});
}
}
#endif
}
else
{
// Tail number always Full - #PrefetchStages
if(tail_num == ck_tile::TailNumber::Full)
#elif(CK_TILE_PIPELINE_DEFAULT == CK_TILE_PIPELINE_COMPUTE_V4)
if(tail_num == ck_tile::TailNumber::Three)
{
Run(ck_tile::bool_constant<false>{},
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Full>{});
Run(ck_tile::bool_constant<true>{},
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Three>{});
}
else
{
std::ostringstream err;
err << "When there's no hot loop, this tail number \"" << tail_num
<< "\" is not supported! PrefetchStages: " << BaseGemmPipeline::PrefetchStages
<< "\n File: " << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
Run(ck_tile::bool_constant<true>{},
ck_tile::integral_constant<ck_tile::TailNumber, ck_tile::TailNumber::Two>{});
}
#endif
}
else
{
std::ostringstream err;
err << "Num K loop must be larger than number of prefetech stages."
<< "\n PrefetchStages: " << BaseGemmPipeline::PrefetchStages << "\n File: " << __FILE__
<< ":" << __LINE__ << ", in function: " << __func__;
throw std::runtime_error(err.str());
}
return ave_time;
......
example/ck_tile/15_fused_moe/fused_moe.hpp
View file @ b75216fa
......@@ -8,14 +8,15 @@
struct fused_moe_args
{
const void* a_ptr; // [m, k], input token
const void* a_scale_ptr; // [m, 1], token scale
const void* g_ptr; // [e, n, k]/[e, 2*n, k], pre-shuffle([e, nr, kr, w])
const void* d_ptr; // [e, n, k], pre-shuffle([e, nr, kr, w])
const void* g_scale_ptr; // [e, 1, n], gate(up) scale
const void* d_scale_ptr; // [e, 1, k], down scale
const void* y_smooth_scale_ptr; // [e, 1, n], smooth-quant-scale for 2nd gemm input
void* o_ptr; // [m, k], output token (no need to do zeroing)
const void* a_ptr; // [m, k], input token
const void* a_scale_ptr; // [m, 1], token scale
const void* g_ptr; // [e, n, k]/[e, 2*n, k], pre-shuffle([e, nr, kr, w])
const void* d_ptr; // [e, n, k], pre-shuffle([e, nr, kr, w])
const void* g_scale_ptr; // [e, 1, n], gate(up) scale
const void* d_scale_ptr; // [e, 1, k], down scale
const void* y_smooth_scale_ptr; // [e, 1, n], smooth-quant-scale for 2nd gemm input
const void* local_expert_mask_ptr; // [e], local_expert_mask_ptr for EP
void* o_ptr; // [m, k], output token (no need to do zeroing)
const void* topk_ids_ptr; // [tokens, topk]
const void* topk_weight_ptr; // [tokens, topk]
......@@ -48,6 +49,8 @@ struct fused_moe_traits
int activation; // 0:gelu, 1:silu
int gate_only; // 0:g1u0, 1:g1u1
int fused_quant; // 0:no-sweep, 1:smooth-dynamic-quant, 2:dynamic-quant
bool local_expert_masking; // if mask experts as local expert
};
float fused_moe(fused_moe_traits, fused_moe_args, const ck_tile::stream_config&);
example/ck_tile/15_fused_moe/fused_moesorting.hpp
View file @ b75216fa
......@@ -10,7 +10,8 @@
struct fused_moesorting_trait
{
std::string index_type;
std::string weight_type; // currently always float
std::string weight_type; // currently always float
bool local_expert_masking; // if mask experts as local expert
};
struct fused_moesorting_args : public ck_tile::MoeSortingHostArgs
......
example/ck_tile/15_fused_moe/instances/fused_moe_api.cpp
View file @ b75216fa
......@@ -17,10 +17,11 @@ float fused_moe(fused_moe_traits t, fused_moe_args a, const ck_tile::stream_conf
return 1;
}();
auto t0 = fused_moesorting_trait{"int32", "fp32"};
auto t0 = fused_moesorting_trait{"int32", "fp32", t.local_expert_masking};
auto a0 = fused_moesorting_args{
a.topk_ids_ptr, // const void* p_topk_ids;
a.topk_weight_ptr, // const void* p_weights;
a.local_expert_mask_ptr, // const void* p_local_expert_mask;
a.sorted_token_ids_ptr, // void* p_sorted_token_ids;
a.sorted_weight_ptr, // void* p_sorted_weights;
a.sorted_expert_ids_ptr, // void* p_sorted_expert_ids;
......
example/ck_tile/15_fused_moe/instances/fused_moesorting_api.cpp
View file @ b75216fa
......@@ -24,20 +24,63 @@
return ave_time;
#else
#define MOE_SORTING_DISPATCH_(sub_token_tile_, sub_token_onshot_) \
constexpr ck_tile::index_t sub_token_tile = sub_token_tile_; \
constexpr bool sub_token_onshot = sub_token_onshot_; \
using ms_problem = \
ck_tile::MoeSortingProblemEx<index_t, ms_weight_type, sub_token_tile, sub_token_onshot>; \
using kernel = ck_tile::MoeSortingKernel<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
const dim3 blocks = kernel::BlockSize(a); \
const auto lds_bytes = kernel::GetSmemSize(a); \
float ave_time = ck_tile::launch_kernel( \
s, ck_tile::make_kernel(kernel{}, grids, blocks, lds_bytes, kargs)); \
#define MOE_SORTING_DISPATCH_(sub_token_tile_, sub_token_onshot_, local_expert_masking_) \
constexpr ck_tile::index_t sub_token_tile = sub_token_tile_; \
constexpr bool sub_token_onshot = sub_token_onshot_; \
constexpr bool local_expert_masking = local_expert_masking_; \
using ms_problem = ck_tile::MoeSortingProblemEx<index_t, \
ms_weight_type, \
sub_token_tile, \
sub_token_onshot, \
local_expert_masking>; \
using kernel = ck_tile::MoeSortingKernel<ms_problem>; \
auto kargs = kernel::MakeKargs(a); \
const dim3 grids = kernel::GridSize(a); \
const dim3 blocks = kernel::BlockSize(a); \
const auto lds_bytes = kernel::GetSmemSize(a); \
float ave_time = ck_tile::launch_kernel( \
s, ck_tile::make_kernel(kernel{}, grids, blocks, lds_bytes, kargs)); \
return ave_time;
#define MOE_SORTING_DISPATCH_SUB_TOKEN_(row_, sub_token_onshot_, local_expert_masking_) \
if(row_ % 8 == 0) \
{ \
MOE_SORTING_DISPATCH_(8, sub_token_onshot_, local_expert_masking_); \
} \
else if(row_ % 4 == 0) \
{ \
MOE_SORTING_DISPATCH_(4, sub_token_onshot_, local_expert_masking_); \
} \
else if(row_ % 2 == 0) \
{ \
MOE_SORTING_DISPATCH_(2, sub_token_onshot_, local_expert_masking_); \
} \
else \
{ \
MOE_SORTING_DISPATCH_(1, sub_token_onshot_, local_expert_masking_); \
}
#define MOE_SORTING_DISPATCH_SUBTO_(row_, local_expert_masking_) \
if(is_sub_token_onshot) \
{ \
MOE_SORTING_DISPATCH_SUB_TOKEN_(row_, true, local_expert_masking_) \
} \
else \
{ \
MOE_SORTING_DISPATCH_SUB_TOKEN_(row_, false, local_expert_masking_) \
}
#define MOE_SORTING_DISPATCH_EMASK_(row_) \
if(is_local_expert_masking) \
{ \
MOE_SORTING_DISPATCH_SUBTO_(row_, true) \
} \
else \
{ \
MOE_SORTING_DISPATCH_SUBTO_(row_, false) \
}
#endif
#if !MOE_SORTING_USE_EX_KERNEL
......@@ -116,45 +159,10 @@ float fused_moesorting(fused_moesorting_trait t, fused_moesorting_args a, ck_til
auto sub_token_ = r_ - 2;
r_ = (r_ - 2) / 8;
bool is_sub_token_onshot = a.tokens <= sub_token_;
bool is_local_expert_masking = t.local_expert_masking;
(void)c_;
if(is_sub_token_onshot)
{
if(r_ % 8 == 0)
{
MOE_SORTING_DISPATCH_(8, true);
}
else if(r_ % 4 == 0)
{
MOE_SORTING_DISPATCH_(4, true);
}
else if(r_ % 2 == 0)
{
MOE_SORTING_DISPATCH_(2, true);
}
else
{
MOE_SORTING_DISPATCH_(1, true);
}
}
else
{
if(r_ % 8 == 0)
{
MOE_SORTING_DISPATCH_(8, false);
}
else if(r_ % 4 == 0)
{
MOE_SORTING_DISPATCH_(4, false);
}
else if(r_ % 2 == 0)
{
MOE_SORTING_DISPATCH_(2, false);
}
else
{
MOE_SORTING_DISPATCH_(1, false);
}
}
MOE_SORTING_DISPATCH_EMASK_(r_);
// MOE_SORTING_DISPATCH_ETILE(0, 0);
#endif
}
......
example/ck_tile/15_fused_moe/main.cpp
View file @ b75216fa
......@@ -140,28 +140,29 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::index_t activation = arg_parser.get_int("act");
if(stride < 0)
stride = hidden_size;
std::string prec_i = arg_parser.get_str("prec_i");
std::string prec_w = arg_parser.get_str("prec_w");
std::string prec_o = arg_parser.get_str("prec_o");
std::string prec_st = arg_parser.get_str("prec_st");
std::string prec_sw = arg_parser.get_str("prec_sw");
std::string prec_sq = arg_parser.get_str("prec_sq");
std::string prec_kw = arg_parser.get_str("prec_kw");
prec_st = (prec_st == "auto") ? "fp32" : prec_st;
prec_sw = (prec_sw == "auto") ? "fp32" : prec_sw;
prec_sq = (prec_sq == "auto") ? "fp32" : prec_sq;
prec_kw = (prec_kw == "auto") ? "fp32" : prec_kw;
int kname = arg_parser.get_int("kname");
int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup");
int repeat = arg_parser.get_int("repeat");
int fused_quant = arg_parser.get_int("fquant");
int gate_only = arg_parser.get_int("gate_only");
int api = arg_parser.get_int("api");
int balance = arg_parser.get_int("balance");
int tp = arg_parser.get_int("tp");
int init = arg_parser.get_int("init");
uint32_t seed = arg_parser.get_uint32("seed");
std::string prec_i = arg_parser.get_str("prec_i");
std::string prec_w = arg_parser.get_str("prec_w");
std::string prec_o = arg_parser.get_str("prec_o");
std::string prec_st = arg_parser.get_str("prec_st");
std::string prec_sw = arg_parser.get_str("prec_sw");
std::string prec_sq = arg_parser.get_str("prec_sq");
std::string prec_kw = arg_parser.get_str("prec_kw");
prec_st = (prec_st == "auto") ? "fp32" : prec_st;
prec_sw = (prec_sw == "auto") ? "fp32" : prec_sw;
prec_sq = (prec_sq == "auto") ? "fp32" : prec_sq;
prec_kw = (prec_kw == "auto") ? "fp32" : prec_kw;
int kname = arg_parser.get_int("kname");
int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup");
int repeat = arg_parser.get_int("repeat");
int fused_quant = arg_parser.get_int("fquant");
int gate_only = arg_parser.get_int("gate_only");
int api = arg_parser.get_int("api");
int balance = arg_parser.get_int("balance");
int tp = arg_parser.get_int("tp");
int init = arg_parser.get_int("init");
uint32_t seed = arg_parser.get_uint32("seed");
bool local_expert_masking = false; // TODO...
// w0 (Gate+Up or Gate only, N size)
ck_tile::index_t shared_intermediate_size_0 = intermediate_size * (gate_only ? 1 : 2) / tp;
......@@ -230,6 +231,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::HostTensor<YSmoothScaleDataType> sy_host({shared_intermediate_size_1}); // smooth-quant
ck_tile::HostTensor<IndexDataType> topk_ids_host({tokens, topk}); // to be sort
ck_tile::HostTensor<TopkWeightDataType> topk_weight_host({tokens, topk}); // to be sort
ck_tile::HostTensor<IndexDataType> local_expert_mask_host({experts});
int max_num_tokens_padded = topk * tokens + experts * block_m - topk;
ck_tile::HostTensor<IndexDataType> sorted_token_ids_host({max_num_tokens_padded});
......@@ -355,6 +357,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::DeviceMem sg_buf(sg_host);
ck_tile::DeviceMem sd_buf(sd_host);
ck_tile::DeviceMem sy_buf(sy_host);
ck_tile::DeviceMem local_expert_mask_buf(local_expert_mask_host);
ck_tile::DeviceMem o_buf(o_host.get_element_space_size_in_bytes());
ck_tile::DeviceMem topk_ids_buf(topk_ids_host);
......@@ -378,7 +381,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
block_m,
activation,
gate_only,
fused_quant};
fused_quant,
local_expert_masking};
fused_moe_args args{a_buf.GetDeviceBuffer(),
fused_quant != 0 ? sa_buf.GetDeviceBuffer() : nullptr,
......@@ -387,6 +391,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
fused_quant != 0 ? sg_buf.GetDeviceBuffer() : nullptr,
fused_quant != 0 ? sd_buf.GetDeviceBuffer() : nullptr,
fused_quant == 1 ? sy_buf.GetDeviceBuffer() : nullptr,
local_expert_masking ? local_expert_mask_buf.GetDeviceBuffer()
: nullptr,
o_buf.GetDeviceBuffer(),
topk_ids_buf.GetDeviceBuffer(),
topk_weight_buf.GetDeviceBuffer(),
......@@ -442,12 +448,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::reference_moe_sorting<TopkWeightDataType, IndexDataType>(
topk_ids_host,
topk_weight_host,
local_expert_mask_host,
sorted_token_ids_host,
sorted_weight_host,
sorted_expert_ids_host,
num_sorted_tiles_host.mData[0],
experts,
block_m);
block_m,
local_expert_masking);
if(activation == 0)
{
CPU_FUSED_MOE(ck_tile::element_wise::Gelu);
......@@ -472,12 +480,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
ck_tile::reference_moe_sorting<TopkWeightDataType, IndexDataType>(
topk_ids_host,
topk_weight_host,
local_expert_mask_host,
sorted_token_ids_host,
sorted_weight_host,
sorted_expert_ids_host,
num_sorted_tiles_host.mData[0],
experts,
block_m);
block_m,
local_expert_masking);
// done, preparing GPU buffer
ck_tile::DeviceMem a_buf(a_host);
......
include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_two_stage_xdl_cshuffle.hpp
View file @ b75216fa
......@@ -1495,10 +1495,13 @@ struct DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle
// if workspace is not allocated
if(!arg.p_workspace_)
{
std::cerr << "Warning: Workspace for "
"DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle::Argument is not "
"allocated, use SetWorkSpacePointer."
<< std::endl;
if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
{
std::cout << "Warning: Workspace for "
"DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle::Argument is not "
"allocated, use SetWorkSpacePointer."
<< std::endl;
}
return false;
}
if(!ck::is_xdl_supported())
......
include/ck/tensor_operation/gpu/grid/gemm_layernorm/gridwise_gemm_multiple_d_welford_first_half_xdl_cshuffle.hpp
View file @ b75216fa
......@@ -515,9 +515,16 @@ struct GridwiseGemmMultipleDWelfordFirstHalf_xdl_cshuffle
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
// sanity check
constexpr auto lcm_AK1_BK1 = math::lcm(AK1, BK1);
constexpr bool is_single_rate_mfma =
((is_same<ABDataType, half_t>::value || is_same<ABDataType, bhalf_t>::value) &&
lcm_AK1_BK1 <= 4)
? true
: false;
constexpr index_t KPack =
math::max(math::lcm(AK1, BK1),
MfmaSelector<ABDataType, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
math::max(lcm_AK1_BK1,
MfmaSelector<ABDataType, MPerXdl, NPerXdl, ABDataType, is_single_rate_mfma>::
selected_mfma.k_per_blk);
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_gemm_xdl_cshuffle_v1.hpp
View file @ b75216fa
......@@ -448,8 +448,16 @@ struct GridwiseBatchedGemmGemm_Xdl_CShuffle
// acc1[m][o] += acc[m][n] * B1[n][o]
// sanity check
constexpr auto lcm_AK1_BK1 = math::lcm(AK1, BK1);
constexpr bool is_single_rate_mfma =
((is_same<FloatAB, half_t>::value || is_same<FloatAB, bhalf_t>::value) &&
lcm_AK1_BK1 <= 4)
? true
: false;
constexpr index_t KPack = math::max(
math::lcm(AK1, BK1), MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
lcm_AK1_BK1,
MfmaSelector<FloatAB, MPerXdl, NPerXdl, FloatAB, is_single_rate_mfma>::selected_mfma
.k_per_blk);
auto blockwise_gemm = BlockwiseGemmXdlops_v2<
BlockSize,
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_multiple_d_gemm_multiple_d_xdl_cshuffle_v1.hpp
View file @ b75216fa
......@@ -361,10 +361,18 @@ struct GridwiseBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle
const auto M = d0_grid_desc_m_n.GetLength(I0);
const auto N = d0_grid_desc_m_n.GetLength(I1);
constexpr auto mfma =
MfmaSelector<A0B0B1DataType, Gemm0MPerXdl, Gemm0NPerXdl>::selected_mfma;
constexpr auto N3 = mfma.num_groups_per_blk;
constexpr auto N5 = mfma.group_size;
constexpr bool is_single_rate_mfma =
((is_same<A0B0B1DataType, half_t>::value || is_same<A0B0B1DataType, bhalf_t>::value) &&
math::lcm(A0K1, B0K1) <= 4)
? true
: false;
constexpr auto mfma = MfmaSelector<A0B0B1DataType,
Gemm0MPerXdl,
Gemm0NPerXdl,
A0B0B1DataType,
is_single_rate_mfma>::selected_mfma;
constexpr auto N3 = mfma.num_groups_per_blk;
constexpr auto N5 = mfma.group_size;
return transform_tensor_descriptor(
d0_grid_desc_m_n,
make_tuple(make_unmerge_transform(make_tuple(
......@@ -643,9 +651,19 @@ struct GridwiseBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle
// acc1[m][o] += acc[m][n] * B1[n][o]
// sanity check
constexpr index_t KPack = math::max(
math::lcm(A0K1, B0K1),
MfmaSelector<A0B0B1DataType, Gemm0MPerXdl, Gemm0NPerXdl>::selected_mfma.k_per_blk);
constexpr auto lcm_A0K1_B0K1 = math::lcm(A0K1, B0K1);
constexpr bool is_single_rate_mfma =
((is_same<A0B0B1DataType, half_t>::value || is_same<A0B0B1DataType, bhalf_t>::value) &&
lcm_A0K1_B0K1 <= 4)
? true
: false;
constexpr index_t KPack =
math::max(lcm_A0K1_B0K1,
MfmaSelector<A0B0B1DataType,
Gemm0MPerXdl,
Gemm0NPerXdl,
A0B0B1DataType,
is_single_rate_mfma>::selected_mfma.k_per_blk);
auto blockwise_gemm0 = BlockwiseGemmXdlops_v2<
BlockSize,
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_multiple_d_softmax_gemm_xdl_cshuffle_v1.hpp
View file @ b75216fa
......@@ -343,10 +343,16 @@ struct GridwiseBatchedGemmMultipleDSoftmaxGemm_Xdl_CShuffle
const auto M = d0_grid_desc_m_n.GetLength(I0);
const auto N = d0_grid_desc_m_n.GetLength(I1);
constexpr auto mfma = MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma;
constexpr auto N3 = mfma.num_groups_per_blk;
constexpr auto N4 = mfma.num_input_blks;
constexpr auto N5 = mfma.group_size;
constexpr bool is_single_rate_mfma =
((is_same<FloatAB, half_t>::value || is_same<FloatAB, bhalf_t>::value) &&
math::lcm(AK1, BK1) <= 4)
? true
: false;
constexpr auto mfma =
MfmaSelector<FloatAB, MPerXdl, NPerXdl, FloatAB, is_single_rate_mfma>::selected_mfma;
constexpr auto N3 = mfma.num_groups_per_blk;
constexpr auto N4 = mfma.num_input_blks;
constexpr auto N5 = mfma.group_size;
return transform_tensor_descriptor(
d0_grid_desc_m_n,
make_tuple(make_unmerge_transform(
......@@ -552,8 +558,16 @@ struct GridwiseBatchedGemmMultipleDSoftmaxGemm_Xdl_CShuffle
// acc1[m][o] += acc[m][n] * B1[n][o]
// sanity check
constexpr auto lcm_AK1_BK1 = math::lcm(AK1, BK1);
constexpr bool is_single_rate_mfma =
((is_same<FloatAB, half_t>::value || is_same<FloatAB, bhalf_t>::value) &&
lcm_AK1_BK1 <= 4)
? true
: false;
constexpr index_t KPack = math::max(
math::lcm(AK1, BK1), MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
lcm_AK1_BK1,
MfmaSelector<FloatAB, MPerXdl, NPerXdl, FloatAB, is_single_rate_mfma>::selected_mfma
.k_per_blk);
auto blockwise_gemm = BlockwiseGemmXdlops_v2<
BlockSize,
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_xdl_cshuffle_v1.hpp
View file @ b75216fa
......@@ -469,8 +469,16 @@ struct GridwiseBatchedGemmSoftmaxGemm_Xdl_CShuffle
// acc1[m][o] += acc[m][n] * B1[n][o]
// sanity check
constexpr auto lcm_AK1_BK1 = math::lcm(AK1, BK1);
constexpr bool is_single_rate_mfma =
((is_same<FloatAB, half_t>::value || is_same<FloatAB, bhalf_t>::value) &&
lcm_AK1_BK1 <= 4)
? true
: false;
constexpr index_t KPack = math::max(
math::lcm(AK1, BK1), MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
lcm_AK1_BK1,
MfmaSelector<FloatAB, MPerXdl, NPerXdl, FloatAB, is_single_rate_mfma>::selected_mfma
.k_per_blk);
auto blockwise_gemm = BlockwiseGemmXdlops_v2<
BlockSize,
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_bias_add_reduce_xdl_cshuffle_v1.hpp
View file @ b75216fa
......@@ -498,8 +498,16 @@ struct GridwiseGemmBiasAddReduce_k0mk1_k0nk1_mn_xdl_cshuffle_v1
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
// sanity check
constexpr auto lcm_AK1_BK1 = math::lcm(AK1, BK1);
constexpr bool is_single_rate_mfma =
((is_same<FloatAB, half_t>::value || is_same<FloatAB, bhalf_t>::value) &&
lcm_AK1_BK1 <= 4)
? true
: false;
constexpr index_t KPack = math::max(
math::lcm(AK1, BK1), MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
lcm_AK1_BK1,
MfmaSelector<FloatAB, MPerXdl, NPerXdl, FloatAB, is_single_rate_mfma>::selected_mfma
.k_per_blk);
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
......
include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp
View file @ b75216fa
......@@ -464,8 +464,16 @@ struct GridwiseGemmMultipleDMultipleR_k0mk1_k0nk1_mn_xdl_cshuffle_v1
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
// register
// sanity check
constexpr auto lcm_AK1_BK1 = math::lcm(AK1, BK1);
constexpr bool is_single_rate_mfma =
((is_same<FloatAB, half_t>::value || is_same<FloatAB, bhalf_t>::value) &&
lcm_AK1_BK1 <= 4)
? true
: false;
constexpr index_t KPack = math::max(
math::lcm(AK1, BK1), MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
lcm_AK1_BK1,
MfmaSelector<FloatAB, MPerXdl, NPerXdl, FloatAB, is_single_rate_mfma>::selected_mfma
.k_per_blk);
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
BlockSize,
......
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