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Merge commit 'f05e45ba59b76cb6ea83c471860ded65d5fc623f' into develop

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github-actions[bot]
2025-05-12 21:01:10 +00:00
parent daf55de9dd
commit 0bfff09142
14 changed files with 448 additions and 934 deletions
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3
CHANGELOG.md
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@@ -19,8 +19,11 @@ Documentation for Composable Kernel available at [https://rocm.docs.amd.com/proj
### Optimized
* Optimize the gemm multiply multiply preshuffle & lds bypass with Pack of KGroup and better instruction layout. (#2166)
* Added Vectorize Transpose optimization for CK Tile (#2131)
### Fixes
None

9
example/65_gemm_multiply_multiply/gemm_multiply_multiply_xdl_fp8_bpreshuffle.cpp
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@@ -9,7 +9,6 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3_b_preshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
@@ -142,12 +141,12 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultiD_Xdl_CShu
AElementOp, BElementOp, CDEElementOp, GemmSpec, 256,
128, 128, 128,
16, 16,
32, 32,
2, 2,
16, 16,
8, 2,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0,
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0,
1, 1, S<1, 32, 1, 8>, S<8, 8, 1>,
ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, FP8>;
2, 1, S<1, 32, 1, 8>, S<8, 8, 1>,
ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, FP8>;
// clang-format on
int main(int argc, char* argv[])

53
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_b_preshuffle_v1.hpp
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@@ -122,6 +122,7 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v1<BlockGemmPipelineScheduler::I
using Base::B_K1;
using Base::I0;
using Base::I1;
using Base::KGroup;
using Base::KRepeat;
using Base::xdlops_gemm;
using typename Base::HotLoopInstList;
@@ -153,9 +154,9 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v1<BlockGemmPipelineScheduler::I
constexpr index_t M0 = TileDesc_M0_M1_M2_K{}.GetLength(Number<0>{});
constexpr index_t M1 = TileDesc_M0_M1_M2_K{}.GetLength(Number<1>{});
constexpr index_t M2 = TileDesc_M0_M1_M2_K{}.GetLength(Number<2>{});
constexpr index_t K2 = KPack;
constexpr index_t K2 = KPack / KGroup;
constexpr index_t K1 = 64 / NPerXDL;
constexpr index_t K0 = KRepeat;
constexpr index_t K0 = KRepeat * KGroup;
return transform_tensor_descriptor(
TileDesc_M0_M1_M2_K{},
@@ -280,12 +281,14 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v1<BlockGemmPipelineScheduler::I
block_sync_lds();
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, k0, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, I0),
a_thread_buf);
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
});
@@ -346,14 +349,18 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v1<BlockGemmPipelineScheduler::I
block_sync_lds();
// loop prefetch copy
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, k0, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, I0),
a_thread_buf);
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
});
@@ -409,14 +416,18 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v1<BlockGemmPipelineScheduler::I
block_sync_lds();
// tail Local Prefetch A1
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, k0, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, I0),
a_thread_buf);
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
});
@@ -495,7 +506,7 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v1<BlockGemmPipelineScheduler::I
ComputeDataType,
decltype(a_block_desc_m0_m1_m2_k0_k1_k2),
decltype(a_thread_desc_),
Sequence<1, 1, 1, 1, 1, KPack>,
Sequence<1, 1, 1, 1, 1, KPack / KGroup>,
Sequence<0, 1, 2, 3, 4, 5>,
5,
A_K1,

68
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_b_preshuffle_v2.hpp
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@@ -122,6 +122,7 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v2<BlockGemmPipelineScheduler::I
using Base::B_K1;
using Base::I0;
using Base::I1;
using Base::KGroup;
using Base::KRepeat;
using Base::xdlops_gemm;
using typename Base::HotLoopInstList;
@@ -152,9 +153,9 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v2<BlockGemmPipelineScheduler::I
constexpr index_t M0 = TileDesc_M0_M1_M2_K{}.GetLength(Number<0>{});
constexpr index_t M1 = TileDesc_M0_M1_M2_K{}.GetLength(Number<1>{});
constexpr index_t M2 = TileDesc_M0_M1_M2_K{}.GetLength(Number<2>{});
constexpr index_t K2 = KPack;
constexpr index_t K2 = KPack / KGroup;
constexpr index_t K1 = 64 / NPerXDL;
constexpr index_t K0 = KRepeat;
constexpr index_t K0 = KRepeat * KGroup;
return transform_tensor_descriptor(
TileDesc_M0_M1_M2_K{},
@@ -281,12 +282,14 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v2<BlockGemmPipelineScheduler::I
block_sync_lds();
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, k0, I0, I0),
a_block_buf.At(I0),
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, I0),
a_thread_bufs(I0));
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
});
@@ -318,14 +321,18 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v2<BlockGemmPipelineScheduler::I
b_thread_bufs(local_read_buf));
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
// main loop A matrix prefetch
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, k0, I0, I0),
a_block_buf.At(local_read_buf),
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, I0),
a_thread_bufs(local_read_buf));
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
});
@@ -389,14 +396,18 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v2<BlockGemmPipelineScheduler::I
b_thread_bufs(local_read_reg));
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
// tail prefetch A
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, k0, I0, I0),
a_block_buf.At(local_read_reg),
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, I0),
a_thread_bufs(local_read_reg));
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
});
@@ -445,12 +456,15 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v2<BlockGemmPipelineScheduler::I
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, k0, I0, I0),
a_block_buf.At(local_read_reg),
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, I0),
a_thread_bufs(local_read_reg));
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf,
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
});
@@ -539,7 +553,7 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v2<BlockGemmPipelineScheduler::I
ComputeDataType,
decltype(a_block_desc_m0_m1_m2_k0_k1_k2),
decltype(a_thread_desc_),
Sequence<1, 1, 1, 1, 1, KPack>,
Sequence<1, 1, 1, 1, 1, KPack / KGroup>,
Sequence<0, 1, 2, 3, 4, 5>,
5,
A_K1,

708
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_b_preshuffle_v3.hpp
View File

@@ -5,6 +5,16 @@
#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp"
#define DS_READ_A_PREFETCH_STAGES 2
template <typename T>
constexpr auto compute_stage_loads(T total_loads, T stages)
{
return std::make_pair((total_loads + stages - 1) / stages, // ceil
total_loads / stages // floor
);
}
namespace ck {
// Compute optimized pipeline
@@ -123,6 +133,7 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
using Base::I0;
using Base::I1;
using Base::I2;
using Base::KGroup;
using Base::KRepeat;
using Base::xdlops_gemm;
using typename Base::HotLoopInstList;
@@ -139,10 +150,6 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
using Base::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
using Base::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
using Base::AMmaKStride;
using Base::BMmaKStride;
using Base::MWaves;
static constexpr index_t PrefetchStages = 2;
@@ -156,9 +163,9 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
constexpr index_t M0 = TileDesc_M0_M1_M2_K{}.GetLength(Number<0>{});
constexpr index_t M1 = TileDesc_M0_M1_M2_K{}.GetLength(Number<1>{});
constexpr index_t M2 = TileDesc_M0_M1_M2_K{}.GetLength(Number<2>{});
constexpr index_t K2 = KPack;
constexpr index_t K2 = KPack / KGroup;
constexpr index_t K1 = 64 / NPerXDL;
constexpr index_t K0 = KRepeat;
constexpr index_t K0 = KRepeat * KGroup;
return transform_tensor_descriptor(
TileDesc_M0_M1_M2_K{},
@@ -184,298 +191,132 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
return num_loop % 2 == 0 ? TailNumber::Even : TailNumber::Odd;
}
template <typename Stage>
__device__ static constexpr auto HotLoopScheduler(Stage stage)
__device__ static constexpr auto HotLoopScheduler()
{
constexpr auto num_ds_read_inst_a = HotLoopInstList::A_LDS_Read_Inst_Num;
constexpr auto num_ds_write_inst_a = HotLoopInstList::A_LDS_Write_Inst_Num;
constexpr auto num_ds_read_inst_a =
HotLoopInstList::A_LDS_Read_Width * sizeof(ADataType) == 16
? HotLoopInstList::A_LDS_Read_Inst_Num
: HotLoopInstList::A_LDS_Read_Inst_Num / 2;
constexpr auto num_ds_write_inst_a = HotLoopInstList::A_LDS_Write_Inst_Num;
constexpr auto num_buffer_load_inst_a = HotLoopInstList::A_Buffer_Load_Inst_Num;
constexpr auto num_buffer_load_inst_b = MWaves * HotLoopInstList::B_Buffer_Load_Inst_Num;
constexpr auto num_buffer_load_inst_b = HotLoopInstList::B_Buffer_Load_Inst_Num;
constexpr auto num_mfma = HotLoopInstList::C_MFMA_Inst_Num;
static_assert(num_buffer_load_inst_a == num_ds_write_inst_a);
constexpr auto staged_num_ds_read_inst_a = num_ds_read_inst_a / MRepeat;
constexpr auto staged_num_mfma = num_mfma / MRepeat;
constexpr auto num_mfma_inst = HotLoopInstList::C_MFMA_Inst_Num;
constexpr auto mfma_cycle = HotLoopInstList::C_MFMA_Inst_Cycle;
constexpr auto staged_num_mfma_per_ds_read_a = staged_num_mfma / staged_num_ds_read_inst_a;
constexpr auto ds_read_a_issue_cycle =
HotLoopInstList::A_LDS_Read_Width * sizeof(ADataType) == 16 ? 8 : 4;
constexpr auto ds_read_a_mfma_rate =
math::integer_divide_ceil(mfma_cycle - 4, 2 * ds_read_a_issue_cycle);
if constexpr(stage.value == 0)
{
constexpr auto staged_num_buffer_load_b_per_ds_read_a =
num_buffer_load_inst_b / staged_num_ds_read_inst_a;
constexpr auto staged_num_mfma_per_buffer_load_b =
staged_num_mfma / num_buffer_load_inst_b;
// B global
static_for<0, staged_num_ds_read_inst_a, 1>{}([&](auto i_inst) {
ignore = i_inst;
constexpr auto num_total_stages = MRepeat;
static_for<0, staged_num_buffer_load_b_per_ds_read_a - 1, 1>{}([&](auto ibuf_inst) {
ignore = ibuf_inst;
// Group num_mfma_perstage num_ds_read_a_perstage
// since we want to reuse a local register buffer
constexpr auto num_mfma_perstage = num_mfma_inst / MRepeat;
constexpr auto num_ds_read_a_perstage = num_ds_read_inst_a / MRepeat;
constexpr auto num_ds_read_a_mfma_perstage =
math::integer_divide_ceil(num_ds_read_a_perstage, ds_read_a_mfma_rate);
constexpr auto total_buffer_loads = num_buffer_load_inst_a + num_buffer_load_inst_b;
constexpr auto stages_available = MRepeat - DS_READ_A_PREFETCH_STAGES;
constexpr auto stage_loads = compute_stage_loads(total_buffer_loads, stages_available);
constexpr auto buffer_load_perstage_more = stage_loads.first;
constexpr auto buffer_load_perstage_less = stage_loads.second;
constexpr auto buffer_load_stages_more = total_buffer_loads % stages_available;
constexpr auto buffer_b_heavy_loads = buffer_load_perstage_more * buffer_load_stages_more;
constexpr auto buffer_b_remaining =
num_buffer_load_inst_b - buffer_load_perstage_more * buffer_load_stages_more;
constexpr auto buffer_load_b_stages =
buffer_b_heavy_loads > num_buffer_load_inst_b
? num_buffer_load_inst_b / buffer_load_perstage_more
: (buffer_load_stages_more + buffer_b_remaining / buffer_load_perstage_less);
constexpr auto buffer_load_a_stages =
num_total_stages - DS_READ_A_PREFETCH_STAGES - buffer_load_b_stages;
static_assert(buffer_load_a_stages > 0,
"The buffer load a stages should always have a value over 0.");
constexpr auto buffer_load_issue_point_interval_more =
math::integer_divide_ceil(num_mfma_perstage, buffer_load_perstage_more);
constexpr auto buffer_load_issue_point_interval_less =
buffer_load_perstage_less == 0
? INT32_MAX
: math::integer_divide_ceil(num_mfma_perstage, buffer_load_perstage_less);
constexpr auto buffer_load_issue_point_a = num_mfma_perstage >= 3 ? 1 : 0;
// B global read
static_for<0, buffer_load_b_stages, 1>{}([&](auto i) {
static_for<0, num_mfma_perstage, 1>{}([&](auto imfma) {
__builtin_amdgcn_sched_group_barrier(SCHED_GROUP_MFMA, 1, 0);
if constexpr(((i < buffer_load_stages_more) &&
(imfma % buffer_load_issue_point_interval_more == 0)) ||
((i >= buffer_load_stages_more) &&
(imfma % buffer_load_issue_point_interval_less == 0)))
{
__builtin_amdgcn_sched_group_barrier(SCHED_GROUP_VMEM, 1, 0);
}
if constexpr(imfma >= (num_mfma_perstage - num_ds_read_a_mfma_perstage))
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_buffer_load_b, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
});
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_buffer_load_b - 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
});
__builtin_amdgcn_sched_barrier(0);
}
else if constexpr(stage.value == 1)
{
constexpr auto staged_num_mfma_per_ds_write_a =
math::integer_divide_ceil(staged_num_mfma, num_ds_write_inst_a);
constexpr auto stage_more_mfma =
staged_num_mfma - (staged_num_mfma_per_ds_write_a - 1) * num_ds_write_inst_a;
// A local write
static_for<0, num_ds_write_inst_a, 1>{}([&](auto i_inst) {
if constexpr(i_inst.value < stage_more_mfma)
{
if(i_inst.value < staged_num_ds_read_inst_a)
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_write_a - 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS Write
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
}
else
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_write_a, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS Write
}
}
else
{
if(i_inst.value < staged_num_ds_read_inst_a)
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_write_a - 2, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS Write
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
}
else
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_write_a - 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS Write
}
SCHED_GROUP_LDS_READ, ds_read_a_mfma_rate, 0);
}
});
__builtin_amdgcn_sched_barrier(0);
}
else if constexpr(stage.value == 2)
{
constexpr auto staged_num_mfma_per_buffer_load_a =
math::integer_divide_ceil(staged_num_mfma, num_buffer_load_inst_a);
constexpr auto stage_more_mfma =
staged_num_mfma - (staged_num_mfma_per_buffer_load_a - 1) * num_buffer_load_inst_a;
// A global
static_for<0, num_buffer_load_inst_a, 1>{}([&](auto i_inst) {
if constexpr(i_inst.value < stage_more_mfma)
{
if(i_inst.value < staged_num_ds_read_inst_a)
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_buffer_load_a - 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
}
else
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_buffer_load_a, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
}
}
else
{
if(i_inst.value < staged_num_ds_read_inst_a)
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_buffer_load_a - 2, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
}
else
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_buffer_load_a - 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
}
}
});
__builtin_amdgcn_sched_barrier(0);
}
else
{
// A local Read
static_for<0, staged_num_ds_read_inst_a, 1>{}([&](auto i_inst) {
ignore = i_inst;
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_read_a, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
});
__builtin_amdgcn_sched_barrier(0);
}
}
template <typename Stage>
__device__ static constexpr auto EpilogueScheduler_1(Stage stage)
{
constexpr auto num_ds_read_inst_a = HotLoopInstList::A_LDS_Read_Inst_Num;
constexpr auto num_ds_write_inst_a = HotLoopInstList::A_LDS_Write_Inst_Num;
constexpr auto num_buffer_load_inst_b = MWaves * HotLoopInstList::B_Buffer_Load_Inst_Num;
constexpr auto num_mfma = HotLoopInstList::C_MFMA_Inst_Num;
constexpr auto staged_num_ds_read_inst_a = num_ds_read_inst_a / MRepeat;
constexpr auto staged_num_mfma = num_mfma / MRepeat;
constexpr auto staged_num_mfma_per_ds_read_a = staged_num_mfma / staged_num_ds_read_inst_a;
if constexpr(stage.value == 0)
{
constexpr auto staged_num_buffer_load_b_per_ds_read_a =
num_buffer_load_inst_b / staged_num_ds_read_inst_a;
constexpr auto staged_num_mfma_per_buffer_load_b =
staged_num_mfma / num_buffer_load_inst_b;
// B global
static_for<0, staged_num_ds_read_inst_a, 1>{}([&](auto i_inst) {
ignore = i_inst;
static_for<0, staged_num_buffer_load_b_per_ds_read_a, 1>{}([&](auto ibuf_inst) {
ignore = ibuf_inst;
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_buffer_load_b, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
});
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_buffer_load_b - 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
});
__builtin_amdgcn_sched_barrier(0);
}
else if constexpr(stage.value == 1)
{
#if 0
constexpr auto staged_num_ds_write_a_per_ds_read_a =
num_ds_write_inst_a / staged_num_ds_read_inst_a;
constexpr auto staged_num_mfma_per_ds_write_a = staged_num_mfma / num_ds_write_inst_a;
// A local write
static_for<0, staged_num_ds_read_inst_a, 1>{}([&](auto i_inst) {
ignore = i_inst;
static_for<0, staged_num_ds_write_a_per_ds_read_a, 1>{}([&](auto idswrite_inst) {
ignore = idswrite_inst;
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_write_a - 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS Write
});
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_ds_write_a_per_ds_read_a, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
});
#elif 1
constexpr auto staged_num_mfma_per_ds_write_a =
math::integer_divide_ceil(staged_num_mfma, num_ds_write_inst_a);
constexpr auto stage_more_mfma =
staged_num_mfma - (staged_num_mfma_per_ds_write_a - 1) * num_ds_write_inst_a;
// A local write
static_for<0, num_ds_write_inst_a, 1>{}([&](auto i_inst) {
if constexpr(i_inst.value < stage_more_mfma)
{
if(i_inst.value < staged_num_ds_read_inst_a)
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_write_a - 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS Write
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
}
else
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_write_a, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS Write
}
}
else
{
if(i_inst.value < staged_num_ds_read_inst_a)
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_write_a - 2, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS Write
__builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
}
else
{
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_write_a - 1, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS Write
}
}
});
#endif
__builtin_amdgcn_sched_barrier(0);
}
else
{
// A local Read
static_for<0, staged_num_ds_read_inst_a, 1>{}([&](auto i_inst) {
ignore = i_inst;
__builtin_amdgcn_sched_group_barrier(
0x008, staged_num_mfma_per_ds_read_a, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
});
__builtin_amdgcn_sched_barrier(0);
}
}
__device__ static constexpr auto EpilogueScheduler_2()
{
constexpr auto num_ds_read_inst_a = HotLoopInstList::A_LDS_Read_Inst_Num;
constexpr auto num_mfma = HotLoopInstList::C_MFMA_Inst_Num;
constexpr auto staged_num_ds_read_inst_a = num_ds_read_inst_a / MRepeat;
constexpr auto staged_num_mfma = num_mfma / MRepeat;
constexpr auto staged_num_mfma_per_ds_read_a = staged_num_mfma / staged_num_ds_read_inst_a;
// A local Read
static_for<0, staged_num_ds_read_inst_a, 1>{}([&](auto i_inst) {
ignore = i_inst;
__builtin_amdgcn_sched_group_barrier(0x008, staged_num_mfma_per_ds_read_a, 0); // MFMA
__builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
});
__builtin_amdgcn_sched_barrier(0);
// A global read + A local write
static_for<0, buffer_load_a_stages, 1>{}([&](auto i) {
static_for<0, num_mfma_perstage, 1>{}([&](auto imfma) {
__builtin_amdgcn_sched_group_barrier(SCHED_GROUP_MFMA, 1, 0);
if constexpr((((i + buffer_load_b_stages) < buffer_load_stages_more) &&
(imfma % buffer_load_issue_point_interval_more == 0)) ||
(((i + buffer_load_b_stages) >= buffer_load_stages_more) &&
(imfma % buffer_load_issue_point_interval_less == 0)))
{
__builtin_amdgcn_sched_group_barrier(SCHED_GROUP_LDS_WRITE, 1, 0);
}
if constexpr((((i + buffer_load_b_stages) < buffer_load_stages_more) &&
(imfma % buffer_load_issue_point_interval_more ==
buffer_load_issue_point_a)) ||
(((i + buffer_load_b_stages) >= buffer_load_stages_more) &&
(imfma % buffer_load_issue_point_interval_less ==
buffer_load_issue_point_a)))
{
__builtin_amdgcn_sched_group_barrier(SCHED_GROUP_VMEM, 1, 0);
}
if constexpr(imfma >= (num_mfma_perstage - num_ds_read_a_mfma_perstage))
{
__builtin_amdgcn_sched_group_barrier(
SCHED_GROUP_LDS_READ, ds_read_a_mfma_rate, 0);
}
});
});
// lds synchronization, prefetch next loop local A
static_for<0, DS_READ_A_PREFETCH_STAGES, 1>{}([&](auto i) {
ignore = i;
static_for<0, num_mfma_perstage, 1>{}([&](auto imfma) {
__builtin_amdgcn_sched_group_barrier(SCHED_GROUP_MFMA, 1, 0);
if constexpr(imfma >= (num_mfma_perstage - num_ds_read_a_mfma_perstage))
{
__builtin_amdgcn_sched_group_barrier(
SCHED_GROUP_LDS_READ, ds_read_a_mfma_rate, 0);
}
});
});
}
template <bool HasMainLoop,
@@ -528,22 +369,27 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
__builtin_amdgcn_sched_barrier(0);
// // Local prefill A1
// Local prefill A1
a_blockwise_copy.RunWrite(a_block_desc, a_block_buf.At(I0));
// // Global prefetch A2
// Global prefetch A2
a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
// Local prefetch A1
block_sync_lds();
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(I0, I0, I0, k0, I0, I0),
a_block_buf.At(I0),
a_thread_desc_,
make_tuple(I0, I0, I0, k0, I0, I0),
a_thread_buf);
static_for<0, DS_READ_A_PREFETCH_STAGES, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, KGroup, 1>{}([&](auto kg0) {
// K = k0 × KGroup × k1 = k0 × kg0 × A_K1
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(m0, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf.At(I0),
a_thread_desc_,
make_tuple(m0, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
});
// Initialize C
@@ -558,26 +404,18 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
do
{
auto LoopFunc = [&](auto mfma_reg_buf, auto local_read_buf) {
static_for<0, MRepeat, 1>{}([&](auto m0) {
if constexpr(m0.value == 0)
{
b_blockwise_copy.Run(b_grid_desc,
b_grid_buf,
b_block_desc_n0_n1_k0_k1,
b_block_origin_idx,
b_thread_bufs(local_read_buf));
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
}
else if constexpr(m0.value == 1)
{
a_blockwise_copy.RunWrite(a_block_desc, a_block_buf.At(local_read_buf));
}
else if constexpr(m0.value == 2)
{
a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
}
b_blockwise_copy.Run(b_grid_desc,
b_grid_buf,
b_block_desc_n0_n1_k0_k1,
b_block_origin_idx,
b_thread_bufs(local_read_buf));
b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
a_blockwise_copy.RunWrite(a_block_desc, a_block_buf.At(local_read_buf));
a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
@@ -613,49 +451,88 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
});
});
if constexpr(m0.value == MRepeat - 1)
if constexpr(m0.value == (MRepeat - 2))
{
block_sync_lds();
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<(m0 + 1) % MRepeat>{}, I0, I0, k0, I0, I0),
a_block_buf.At(local_read_buf),
a_thread_desc_,
make_tuple(
Number<(m0 + 1 + HotloopLocalBufSwitch * mfma_reg_buf) %
2>{},
I0,
I0,
k0,
I0,
I0),
a_thread_buf);
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<0>{},
I0,
I0,
Number<k0 * KGroup + kg0>{},
I0,
I0),
a_block_buf.At(local_read_buf),
a_thread_desc_,
make_tuple(
Number<(m0 + 2 + HotloopLocalBufSwitch * mfma_reg_buf) %
2>{},
I0,
I0,
k0,
I0,
Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
}
else if constexpr(m0.value == (MRepeat - 1))
{
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<(m0 + 2) % MRepeat>{},
I0,
I0,
Number<k0 * KGroup + kg0>{},
I0,
I0),
a_block_buf.At(local_read_buf),
a_thread_desc_,
make_tuple(
Number<(m0 + 2 + HotloopLocalBufSwitch * mfma_reg_buf) %
2>{},
I0,
I0,
k0,
I0,
Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
}
else
{
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<(m0 + 1) % MRepeat>{}, I0, I0, k0, I0, I0),
a_block_buf.At(mfma_reg_buf),
a_thread_desc_,
make_tuple(
Number<(m0 + 1 + HotloopLocalBufSwitch * mfma_reg_buf) %
2>{},
I0,
I0,
k0,
I0,
I0),
a_thread_buf);
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<(m0 + 2) % MRepeat>{},
I0,
I0,
Number<k0 * KGroup + kg0>{},
I0,
I0),
a_block_buf.At(mfma_reg_buf),
a_thread_desc_,
make_tuple(
Number<(m0 + 2 + HotloopLocalBufSwitch * mfma_reg_buf) %
2>{},
I0,
I0,
k0,
I0,
Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
}
HotLoopScheduler(m0);
});
HotLoopScheduler();
};
LoopFunc(I0, I1);
@@ -667,20 +544,14 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
// tail
if constexpr(TailNum == TailNumber::Even)
{
static_for<0, MRepeat, 1>{}([&](auto m0) {
if constexpr(m0.value == 0)
{
b_blockwise_copy.Run(b_grid_desc,
b_grid_buf,
b_block_desc_n0_n1_k0_k1,
b_block_origin_idx,
b_thread_bufs(I1));
}
else if constexpr(m0.value == MRepeat - 1)
{
a_blockwise_copy.RunWrite(a_block_desc, a_block_buf.At(I1));
}
b_blockwise_copy.Run(b_grid_desc,
b_grid_buf,
b_block_desc_n0_n1_k0_k1,
b_block_origin_idx,
b_thread_bufs(I1));
a_blockwise_copy.RunWrite(a_block_desc, a_block_buf.At(I1));
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
vector_type<ComputeDataType, KPack> a_thread_vec;
@@ -707,36 +578,68 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
});
});
if constexpr(m0.value == MRepeat - 1)
if constexpr(m0.value == (MRepeat - 2))
{
block_sync_lds();
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<(m0 + 1) % MRepeat>{}, I0, I0, k0, I0, I0),
a_block_buf.At(I1),
a_thread_desc_,
make_tuple(Number<(m0 + 1) % 2>{}, I0, I0, k0, I0, I0),
a_thread_buf);
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(
Number<0>{}, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf.At(I1),
a_thread_desc_,
make_tuple(
Number<(m0 + 2) % 2>{}, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
}
else if constexpr(m0.value == (MRepeat - 1))
{
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<(m0 + 2) % MRepeat>{},
I0,
I0,
Number<k0 * KGroup + kg0>{},
I0,
I0),
a_block_buf.At(I1),
a_thread_desc_,
make_tuple(
Number<(m0 + 2) % 2>{}, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
}
else
{
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<(m0 + 1) % MRepeat>{}, I0, I0, k0, I0, I0),
a_block_buf.At(I0),
a_thread_desc_,
make_tuple(Number<(m0 + 1) % 2>{}, I0, I0, k0, I0, I0),
a_thread_buf);
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<(m0 + 2) % MRepeat>{},
I0,
I0,
Number<k0 * KGroup + kg0>{},
I0,
I0),
a_block_buf.At(I0),
a_thread_desc_,
make_tuple(
Number<(m0 + 2) % 2>{}, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
}
EpilogueScheduler_1(m0);
});
HotLoopScheduler();
static_for<0, MRepeat, 1>{}([&](auto m0) {
static_for<0, KRepeat, 1>{}([&](auto k0) {
static_for<0, NRepeat, 1>{}([&](auto n0) {
@@ -764,25 +667,29 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
});
});
if constexpr(m0.value != (MRepeat - 1))
if constexpr(m0.value < (MRepeat - 2))
{
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<m0 + 1>{}, I0, I0, k0, I0, I0),
a_block_buf.At(I1),
a_thread_desc_,
make_tuple(
Number<(m0 + 1 + HotloopLocalBufSwitch) % 2>{}, I0, I0, k0, I0, I0),
a_thread_buf);
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(
Number<m0 + 2>{}, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf.At(I1),
a_thread_desc_,
make_tuple(Number<(m0 + 2 + HotloopLocalBufSwitch) % 2>{},
I0,
I0,
k0,
I0,
Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
EpilogueScheduler_2();
}
});
// Let's leak last MFMA block to epilogue region, cover the potential lds-shuffle
// latency
// __builtin_amdgcn_sched_barrier(0);
HotLoopScheduler();
}
else if constexpr(TailNum == TailNumber::Odd)
{
@@ -813,18 +720,21 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
});
});
if constexpr(m0.value != (MRepeat - 1))
if constexpr(m0.value < (MRepeat - 2))
{
static_for<0, KRepeat, 1>{}([&](auto k0) {
a_thread_copy_.Run(a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(Number<m0 + 1>{}, I0, I0, k0, I0, I0),
a_block_buf.At(I0),
a_thread_desc_,
make_tuple(Number<(m0 + 1) % 2>{}, I0, I0, k0, I0, I0),
a_thread_buf);
static_for<0, KGroup, 1>{}([&](auto kg0) {
a_thread_copy_.Run(
a_block_desc_m0_m1_m2_k0_k1_k2,
make_tuple(
Number<m0 + 2>{}, I0, I0, Number<k0 * KGroup + kg0>{}, I0, I0),
a_block_buf.At(I0),
a_thread_desc_,
make_tuple(
Number<(m0 + 2) % 2>{}, I0, I0, k0, I0, Number<kg0 * A_K1>{}),
a_thread_buf);
});
});
EpilogueScheduler_2();
}
});
}
@@ -841,7 +751,7 @@ struct BlockwiseGemmXdlops_pipeline_bpreshuffle_v3<BlockGemmPipelineScheduler::I
ComputeDataType,
decltype(a_block_desc_m0_m1_m2_k0_k1_k2),
decltype(a_thread_desc_),
Sequence<1, 1, 1, 1, 1, KPack>,
Sequence<1, 1, 1, 1, 1, KPack / KGroup>,
Sequence<0, 1, 2, 3, 4, 5>,
5,
A_K1,

5
include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp
View File

@@ -58,6 +58,11 @@ struct BlockwiseGemmXdlops_pipeline_base
static constexpr index_t KPerThread = KPerBlock / xdlops_gemm.K0PerXdlops;
static constexpr index_t KRepeat = KPerThread / KPack;
static constexpr index_t KPerInnerLoop = KPack;
static constexpr index_t KGroup =
((MPerXDL == 16 && MPerXDL == 16 && xdlops_gemm.KPerXdlops == 128) ||
(MPerXDL == 32 && MPerXDL == 32 && xdlops_gemm.KPerXdlops == 64))
? 2
: 1;
static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerXDL);
static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);

16
include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3_multi_d_b_preshuffle.hpp
View File

@@ -167,11 +167,13 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
using mfma_selector = MfmaSelector<ComputeTypeA, MPerXdl, NPerXdl, ComputeTypeB>;
static constexpr index_t KPack =
math::max(math::lcm(AK1Number, BK1Number), mfma_selector::selected_mfma.k_per_blk);
static constexpr index_t KGroup = mfma_selector::selected_mfma.k_per_blk == 32 ? 2 : 1;
static constexpr index_t KLane =
mfma_selector::GetKPerXdlops() / mfma_selector::GetK1PerXdlops();
static constexpr index_t KRepeat = KPerBlock / KLane / KPack;
static constexpr index_t NLane = NPerXdl;
static constexpr index_t NWave = NPerBlock / NPerXdl / NXdlPerWave;
static constexpr index_t KPackPerGroup = KPack / KGroup;
static constexpr index_t KRepeat = KPerBlock / KLane / KPackPerGroup;
static constexpr index_t NLane = NPerXdl;
static constexpr index_t NWave = NPerBlock / NPerXdl / NXdlPerWave;
static constexpr auto MakeDsGridPointer()
{
@@ -209,7 +211,7 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
}
__host__ __device__ static auto CalculateBK0Shuffled(index_t K)
{
return math::integer_divide_ceil(K, KLane * KPack);
return math::integer_divide_ceil(K, KLane * KPackPerGroup);
}
__host__ __device__ static auto CalculateKPadded(index_t K)
@@ -351,7 +353,7 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
__host__ __device__ static auto MakeBGridDescriptor_Preshuffled(index_t N0, index_t K0)
{
constexpr index_t NkSwizzleNumber = Number<warpSize * KPack>{};
constexpr index_t NkSwizzleNumber = Number<warpSize * KPackPerGroup>{};
return make_naive_tensor_descriptor(
make_tuple(N0 / NWave, NWave, K0, NkSwizzleNumber),
make_tuple(NWave * K0 * NkSwizzleNumber, K0 * NkSwizzleNumber, NkSwizzleNumber, I1));
@@ -1228,7 +1230,7 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
make_multi_index(n_block_data_idx_on_grid,
get_warp_local_1d_id() % NWave,
0,
KPack * (get_thread_local_1d_id() % warpSize)));
KPackPerGroup * (get_thread_local_1d_id() % warpSize)));
// LDS allocation for A and B: be careful of alignment
// Cast after lds
@@ -1668,7 +1670,7 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3_b_preshuffle
make_multi_index(n_block_data_idx_on_grid,
get_warp_local_1d_id() % NWave,
0,
KPack * (get_thread_local_1d_id() % warpSize)));
KPackPerGroup * (get_thread_local_1d_id() % warpSize)));
// LDS allocation for A and B: be careful of alignment
// Cast after lds

20
include/ck/utility/blkgemmpipe_scheduler.hpp
View File

@@ -48,6 +48,15 @@ enum struct TailNumber
// prefetchstages
Full,
};
enum SchedulerGroup : uint32_t
{
SCHED_GROUP_MFMA = 0x008, // Matrix FMA instructions
SCHED_GROUP_VMEM = 0x020, // Global memory operations
SCHED_GROUP_LDS_READ = 0x100, // LDS read operations
SCHED_GROUP_LDS_WRITE = 0x200 // LDS write operations
};
template <index_t BlockSize,
index_t MPerBlock,
index_t NPerBlock,
@@ -90,6 +99,17 @@ struct BlockwiseGemmXdlops_pipeline_hotloop_inst
static constexpr index_t C_MFMA_Inst_Num =
MPerBlock * NPerBlock * KPerBlock / (BlockSize / WaveSize) / (MPerXDL * NPerXDL * KPerXDL);
static constexpr index_t C_MFMA_Inst_Cycle = []() {
if constexpr(NPerXDL == 16)
{
return KPerXDL == 128 ? 32 : 16;
}
else if constexpr(NPerXDL == 32)
{
return KPerXDL == 64 ? 64 : 32;
}
}();
static constexpr auto Print()
{
printf(" Blk/Wave Size: %d, %d, M/N/K PerBlk: %d, %d, %d, M/N/K PerXdl: %d, %d, %d\n",

9
include/ck_tile/ops/gemm/warp/warp_gemm.hpp
View File

@@ -109,20 +109,11 @@ using WarpGemmMfmaF16F16F32M64N4K16 = WarpGemmImpl<WarpGemmAtrributeMfmaIterateK
4>>;
// fp16 2:4 structured sparsity
#if defined(__gfx94__) || defined(__gfx95__)
using WarpGemmSmfmacF16F16F32M32N32K16 = WarpGemmSmfmacImpl<WarpGemmAttributeSmfmac<
WarpGemmAttributeSmfmacImplF16F16F32M32N32K16<WGAttrCtlEnum::Default_>>>;
using WarpGemmSmfmacF16F16F32M16N16K32 = WarpGemmSmfmacImpl<WarpGemmAttributeSmfmac<
WarpGemmAttributeSmfmacImplF16F16F32M16N16K32<WGAttrCtlEnum::Default_>>>;
#else // gfx 90a does not support smfmac
using WarpGemmSmfmacF16F16F32M32N32K16 = WarpGemmImpl<WarpGemmAtrributeMfmaIterateK<
WarpGemmAttributeMfmaImplF16F16F32M32N32K8<WGAttrCtlEnum::Default_>,
2>>;
using WarpGemmSmfmacF16F16F32M16N16K32 = WarpGemmImpl<WarpGemmAtrributeMfmaIterateK<
WarpGemmAttributeMfmaImplBf16Bf16F32M16N16K16<WGAttrCtlEnum::Default_>,
2>>;
#endif
// bf16
using WarpGemmMfmaBf16Bf16F32M32N32K8 = WarpGemmImpl<

4
include/ck_tile/ops/gemm/warp/warp_gemm_attribute_smfmac_impl.hpp
View File

@@ -49,7 +49,7 @@ struct WarpGemmAttributeSmfmacImplF16F16F32M32N32K16
const int32_t& idx,
bool_constant<post_nop_> = {}) const
{
#if defined(__gfx9__)
#if defined(__gfx94_) or defined(__gfx95_)
c_vec = __builtin_amdgcn_smfmac_f32_32x32x16_f16(a_vec, b_vec, c_vec, idx, 0, 0);
#else
ck_tile::ignore = c_vec;
@@ -100,7 +100,7 @@ struct WarpGemmAttributeSmfmacImplF16F16F32M16N16K32
const int32_t& idx,
bool_constant<post_nop_> = {}) const
{
#if defined(__gfx9__)
#if defined(__gfx94_) or defined(__gfx95_)
c_vec = __builtin_amdgcn_smfmac_f32_16x16x32_f16(a_vec, b_vec, c_vec, idx, 0, 0);
#else
ck_tile::ignore = c_vec;

389
library/include/ck/library/tensor_operation_instance/gpu/gemm_multiply_multiply_wp.hpp
View File

@@ -18,173 +18,6 @@ namespace device {
namespace instance {
#if(defined(CK_ENABLE_F16) || defined(CK_ENABLE_FP8))
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p1_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p2_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p3_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p4_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p5_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p1_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p2_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p3_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p4_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p5_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_compute_default_instances_p1(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_compute_default_instances_p2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instances_p1(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
@@ -268,174 +101,6 @@ void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma16
#endif
#if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_FP8))
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p1_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p2_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p3_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p4_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p5_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p1_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p2_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p3_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p4_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p5_default_instances_v2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_compute_default_instances_p1(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_compute_default_instances_p2(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
BF16,
PassThrough,
PassThrough,
MultiplyMultiply>>>&
instances);
void add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instances_p1(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitKBPreShuffle<Row,
Col,
@@ -562,33 +227,6 @@ struct DeviceOperationInstanceFactory<
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p1_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p2_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p3_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p4_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p5_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p1_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p2_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p3_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p4_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_p5_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_compute_default_instances_p1(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma_mn_compute_default_instances_p2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instances_p1(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instances_p2(
@@ -612,33 +250,6 @@ struct DeviceOperationInstanceFactory<
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p1_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p2_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p3_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p4_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p5_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p1_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p2_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p3_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p4_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_p5_default_instances_v2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_compute_default_instances_p1(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma_mn_compute_default_instances_p2(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instances_p1(
op_ptrs);
add_device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instances_p2(

48
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply_wp/CMakeLists.txt
View File

@@ -2,18 +2,6 @@
set(GEMM_MULTIPLY_MULTIPLY_WEIGHT_PRESHUFFLE_INSTANCES)
list(APPEND GEMM_MULTIPLY_MULTIPLY_WEIGHT_PRESHUFFLE_INSTANCES
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p1_default_instance.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p2_default_instance.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p3_default_instance.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p4_default_instance.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p5_default_instance.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p1_default_instance_v2.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p2_default_instance_v2.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p3_default_instance_v2.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p4_default_instance_v2.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p5_default_instance_v2.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_compute_default_instance_p1.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_compute_default_instance_p2.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p1.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p2.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p3.cpp
@@ -21,18 +9,6 @@ list(APPEND GEMM_MULTIPLY_MULTIPLY_WEIGHT_PRESHUFFLE_INSTANCES
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p5.cpp
f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p6.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p1_default_instance.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p2_default_instance.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p3_default_instance.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p4_default_instance.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p5_default_instance.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p1_default_instance_v2.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p2_default_instance_v2.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p3_default_instance_v2.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p4_default_instance_v2.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p5_default_instance_v2.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_compute_default_instance_p1.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_compute_default_instance_p2.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instance_p1.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instance_p2.cpp
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instance_p3.cpp
@@ -41,18 +17,6 @@ list(APPEND GEMM_MULTIPLY_MULTIPLY_WEIGHT_PRESHUFFLE_INSTANCES
f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instance_p6.cpp
)
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p1_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p2_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p3_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p4_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p5_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p1_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p2_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p3_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p4_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_p5_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_compute_default_instance_p1.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn_compute_default_instance_p2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p1.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p3.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
@@ -60,18 +24,6 @@ set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p5.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma16x16_mn_compute_default_instance_p6.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p1_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p2_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p3_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p4_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p5_default_instance.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p1_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p2_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p3_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p4_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_p5_default_instance_v2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_compute_default_instance_p1.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma_mn_compute_default_instance_p2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instance_p1.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instance_p2.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")
set_source_files_properties(f8_f8_f16/device_gemm_multiply_multiply_wp_xdl_f8_f8_f16_mk_mfma16x16_mn_compute_default_instance_p3.cpp PROPERTIES COMPILE_OPTIONS ";-mllvm;-greedy-reverse-local-assignment=1")

44
library/src/tensor_operation_instance/gpu/gemm_multiply_multiply_wp/f8_f8_bf16/device_gemm_multiply_multiply_wp_xdl_f8_f8_bf16_mk_mfma_mn.hpp
View File

@@ -171,13 +171,13 @@ using device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma16x1
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// Compute friendly
// 256x[64, 256, 32]x128
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 256, 128, 16, 16, 16, 16, 8, 8, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 256, 128, 16, 16, 16, 16, 16, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 224, 128, 16, 16, 16, 16, 8, 7, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 192, 128, 16, 16, 16, 16, 8, 6, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 192, 128, 16, 16, 16, 16, 16, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 160, 128, 16, 16, 16, 16, 8, 5, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 128, 128, 16, 16, 16, 16, 8, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 128, 128, 16, 16, 16, 16, 16, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 96, 128, 16, 16, 16, 16, 8, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 64, 128, 16, 16, 16, 16, 8, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 256, 64, 128, 16, 16, 16, 16, 16, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
// clang-format on
>;
@@ -190,13 +190,13 @@ using device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma16x1
//############################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// 224x[64, 256, 32]x128
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 256, 128, 16, 16, 16, 16, 7, 8, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 256, 128, 16, 16, 16, 16, 14, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 224, 128, 16, 16, 16, 16, 7, 7, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 192, 128, 16, 16, 16, 16, 7, 6, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 192, 128, 16, 16, 16, 16, 14, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 160, 128, 16, 16, 16, 16, 7, 5, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 128, 128, 16, 16, 16, 16, 7, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 128, 128, 16, 16, 16, 16, 14, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 96, 128, 16, 16, 16, 16, 7, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 64, 128, 16, 16, 16, 16, 7, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 224, 64, 128, 16, 16, 16, 16, 14, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
// clang-format on
>;
template <GemmSpecialization GemmSpec>
@@ -208,13 +208,13 @@ using device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma16x1
//############################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// 192x[64, 256, 32]x128, 192x[64]x256
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 256, 128, 16, 16, 16, 16, 6, 8, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 256, 128, 16, 16, 16, 16, 12, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 224, 128, 16, 16, 16, 16, 6, 7, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 192, 128, 16, 16, 16, 16, 6, 6, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 192, 128, 16, 16, 16, 16, 12, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 160, 128, 16, 16, 16, 16, 6, 5, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 128, 128, 16, 16, 16, 16, 6, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 128, 128, 16, 16, 16, 16, 12, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 96, 128, 16, 16, 16, 16, 6, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 64, 128, 16, 16, 16, 16, 6, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 192, 64, 128, 16, 16, 16, 16, 12, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
// clang-format on
>;
template <GemmSpecialization GemmSpec>
@@ -226,13 +226,13 @@ using device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma16x1
//############################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// 160x[64, 256, 32]x128, 160x[64, 96, 32]x256
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 256, 128, 16, 16, 16, 16, 5, 8, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 256, 128, 16, 16, 16, 16, 10, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 224, 128, 16, 16, 16, 16, 5, 7, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 192, 128, 16, 16, 16, 16, 5, 6, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 192, 128, 16, 16, 16, 16, 10, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 160, 128, 16, 16, 16, 16, 5, 5, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 128, 128, 16, 16, 16, 16, 5, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 128, 128, 16, 16, 16, 16, 10, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 96, 128, 16, 16, 16, 16, 5, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<4, 4, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 64, 128, 16, 16, 16, 16, 5, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 160, 64, 128, 16, 16, 16, 16, 10, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
// clang-format on
>;
template <GemmSpecialization GemmSpec>
@@ -244,10 +244,10 @@ using device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma16x1
//############################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 96, 128, 16, 16, 16, 16, 4, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 64, 128, 16, 16, 16, 16, 4, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 128, 256, 16, 16, 16, 16, 4, 4, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 64, 128, 16, 16, 16, 16, 8, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 128, 256, 16, 16, 16, 16, 8, 2, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 96, 256, 16, 16, 16, 16, 4, 3, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 64, 256, 16, 16, 16, 16, 4, 2, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 64, 256, 16, 16, 16, 16, 8, 1, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
// clang-format on
>;
@@ -259,11 +259,11 @@ using device_gemm_multiply_multiply_weight_preshuffle_xdl_f8_f8_bf16_mk_mfma16x1
//############################################| | | | | Type| Type| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise|Specialization| 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_MXdlPerWave_MWaveMPerXdl| ScalarPerVector| Pipeline| Pipeline|
//############################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 256, 128, 16, 16, 16, 16, 4, 8, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 256, 128, 16, 16, 16, 16, 8, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 224, 128, 16, 16, 16, 16, 4, 7, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 192, 128, 16, 16, 16, 16, 4, 6, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 192, 128, 16, 16, 16, 16, 8, 3, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 160, 128, 16, 16, 16, 16, 4, 5, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 64, 1, 4>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>,
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 128, 128, 16, 16, 16, 16, 4, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 2, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
DeviceGemmMultiD_Xdl_CShuffle_V3_BPreshuffle< Row, Col, Tuple<Row, Col>, Row, F8, F8, Tuple<F32, F32>, BF16, F32, F32, PassThrough, PassThrough, MultiplyMultiply, GemmSpec, 256, 128, 128, 128, 16, 16, 16, 16, 8, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 2, 1, S<1, 32, 1, 8>, S<8, 8, 1>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3, F8>
// clang-format on
>;

6
tile_engine/ops/gemm/gemm_instance_builder.py
View File

@@ -535,11 +535,7 @@ struct GemmDispatcher {
((tile[6] == 32 and tile[7] == 32 and tile[8] == 16) or
(tile[6] == 16 and tile[7] == 16 and tile[8] == 32))
content += f"""
#if defined(__gfx908__)
run_kernel<{group}::GemmKernel<{tile[0]}, {tile[1]}, {tile[2]}, {tile[3]}, {tile[4]}, {tile[5]}, {tile[6]}, {tile[7]}, {tile[8]}, {BOOL_MAP(False)}>>(c_m_n_dev_buf, c_m_n_host_result, c_m_n_dev_result, verify, args, stream);
#else
run_kernel<{group}::GemmKernel<{tile[0]}, {tile[1]}, {tile[2]}, {tile[3]}, {tile[4]}, {tile[5]}, {tile[6]}, {tile[7]}, {tile[8]}, {BOOL_MAP(sparse)}>>(c_m_n_dev_buf, c_m_n_host_result, c_m_n_dev_result, verify, args, stream);
#endif"""
run_kernel<{group}::GemmKernel<{tile[0]}, {tile[1]}, {tile[2]}, {tile[3]}, {tile[4]}, {tile[5]}, {tile[6]}, {tile[7]}, {tile[8]}, {BOOL_MAP(sparse)}>>(c_m_n_dev_buf, c_m_n_host_result, c_m_n_dev_result, verify, args, stream);"""
content += f"""
}} else {{"""
for tile in tile_params: