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Commit 5df713ef authored by aska-0096's avatar aska-0096
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example/32_batched_gemm_scale_softmax_gemm/batched_gemm_scale_softmax_gemm_permute_wmma_fp16.cpp
View file @ 5df713ef
......@@ -2,7 +2,7 @@
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
/*
Gemm + Softmax + Gemm fused operation. Computes C_g_m_o = Softmax(A_g_m_k * B0_g_k_n) * B1_g_n_o
Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g_k_l) * B1_g_l_n
|-----------------|
Gemm0
|-------------------------------------|
......@@ -39,7 +39,8 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16;
using B0DataType = F16;
using B1DataType = F16;
using AccDataType = F32;
using Acc0DataType = F32;
using Acc1DataType = F32;
using CShuffleDataType = F32;
using CDataType = F16;
using Acc0BiasDataType = ck::Tuple<>;
......@@ -67,7 +68,7 @@ static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecial
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Xdl_CShuffle<
ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
NumDimG,
NumDimM,
NumDimN,
......@@ -76,11 +77,12 @@ using DeviceGemmInstance =
ADataType,
B0DataType,
B1DataType,
CDataType,
Acc0BiasDataType,
Acc0DataType,
Acc1BiasDataType,
AccDataType,
Acc1DataType,
CShuffleDataType,
CDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
......@@ -91,21 +93,21 @@ using DeviceGemmInstance =
TensorSpecB0,
TensorSpecB1,
TensorSpecC,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
64, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
2, // Gemm1NXdlPerWave
128, // LPerBlock
4, // K0PerBlock
8, // K1
64, // NPerBlock
4, // L0PerBlock
8, // L1
16, // MPerWMMA
16, // LPerWMMA
16, // NPerWMMA
//Per repeat = wave_m = wave_num, wave_n = 1
1, // MRepeat
8, // LRepeat
4, // NRepeat
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
......@@ -113,44 +115,44 @@ using DeviceGemmInstance =
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<4, 64, 1>, // B0BlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<16, 16, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
S<4, 64, 1>, // B1BlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
1,
4,
2,
8,
8,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
4, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
// Ref Gemm0: fp16 in, fp32 out
using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B0DataType,
AccDataType,
AccDataType,
Acc0DataType,
Acc1DataType,
AElementOp,
B0ElementOp,
Acc0ElementOp>;
// Ref Softmax: fp32 in, fp16 out
using ReferenceSoftmaxInstance =
ck::tensor_operation::host::ReferenceSoftmax<AccDataType, ADataType, AccDataType>;
ck::tensor_operation::host::ReferenceSoftmax<Acc0DataType, ADataType, Acc0DataType>;
// Ref Gemm1: fp16 in, fp16 out
using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B1DataType,
CDataType,
AccDataType,
Acc1DataType,
AElementOp,
B1ElementOp,
CElementOp>;
......
example/32_batched_gemm_scale_softmax_gemm/run_batched_gemm_scale_softmax_gemm_permute.inc
View file @ 5df713ef
......@@ -198,7 +198,7 @@ int run(int argc, char* argv[])
Tensor<ADataType> a_g_m_k({BatchCount, M, K});
Tensor<B0DataType> b0_g_k_n({BatchCount, K, N});
Tensor<B1DataType> b1_g_n_o({BatchCount, N, O});
Tensor<AccDataType> acc0_g_m_n({BatchCount, M, N}); // scratch object after gemm0
Tensor<Acc0DataType> acc0_g_m_n({BatchCount, M, N}); // scratch object after gemm0
Tensor<ADataType> a1_g_m_n({BatchCount, M, N}); // scratch object after softmax
Tensor<CDataType> c_g_m_o_host_result({BatchCount, M, O}); // scratch object after gemm1
......
include/ck/tensor_operation/gpu/block/blockwise_gemm_wmma.hpp
View file @ 5df713ef
......@@ -129,11 +129,12 @@ struct BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle
return make_tuple(c_thread_m, c_thread_n);
}
using Tuple5 = decltype(CalculateAThreadOriginDataIndex());
__host__ __device__ BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle(
Tuple4 a_origin = CalculateAThreadOriginDataIndex(),
Tuple4 b_origin = CalculateBThreadOriginDataIndex())
: a_thread_copy_(a_origin), b_thread_copy_(b_origin)
// using Tuple5 = decltype(CalculateAThreadOriginDataIndex());
// __host__ __device__ BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle(
// Tuple4 a_origin = CalculateAThreadOriginDataIndex(),
// Tuple4 b_origin = CalculateBThreadOriginDataIndex())
// : a_thread_copy_(a_origin), b_thread_copy_(b_origin)
__host__ __device__ BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle()
{
static_assert(AK0MK1BlockDesc::IsKnownAtCompileTime() &&
BK0NK1BlockDesc::IsKnownAtCompileTime(),
......@@ -303,8 +304,10 @@ struct BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle
B_K1,
B_K1>;
AThreadCopy a_thread_copy_;
BThreadCopy b_thread_copy_;
AThreadCopy a_thread_copy_{CalculateAThreadOriginDataIndex()};
BThreadCopy b_thread_copy_{CalculateBThreadOriginDataIndex()};
// AThreadCopy a_thread_copy_;
// BThreadCopy b_thread_copy_;
};
// block wise level pipe designed for inline asm
......@@ -425,6 +428,25 @@ struct BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle_FIFO
return make_tuple(c_thread_m, c_thread_n);
}
template <index_t m0, index_t n0>
__device__ static auto CalculateCThreadOriginDataIndex7D(Number<m0>, Number<n0>)
{
const auto wave_idx = GetWaveIdx();
const auto waveId_m = wave_idx[I0];
const auto waveId_n = wave_idx[I1];
const auto blk_idx = wmma_gemm.GetBeginOfThreadBlk3D();
return make_tuple(Number<m0>{},
blk_idx[I0],
waveId_m,
Number<n0>{},
waveId_n,
blk_idx[I1],
blk_idx[I2]);
}
__host__ __device__ BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle_FIFO()
{
static_assert(AK0MK1BlockDesc::IsKnownAtCompileTime() &&
......@@ -438,6 +460,30 @@ struct BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle_FIFO
NPerBlock % (NPerWMMA * NRepeat) == 0,
"wrong!");
}
// transposed WMMA output C' = B' * A'
__host__ __device__ static constexpr auto
GetCThreadDescriptor_MRepeat_MWave_MThreadPerSubGroup_NRepeat_NWave_NSubGroup_NAccVgprs()
{
constexpr auto c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens =
wmma_gemm.GetCMSubGroupNThreadPerSubGroupMAccVgprsThreadBlkLengths();
// constexpr auto NSubGroup = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I0];
// constexpr auto MThreadPerSubGroup = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I1];
constexpr auto NAccVgprs = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I2];
return make_naive_tensor_descriptor_packed(
// |MRepeat |MWave |MSubGroup |NRepeat |NWave
// |NThreadPerSubGroup |MAccVgprs
make_tuple(Number<MRepeat>{},
I1,
I1,
Number<NRepeat>{},
I1,
I1,
NAccVgprs));
}
// Thread level, register decriptor. Vector-write
__host__ __device__ static constexpr auto
GetCThreadDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs()
......@@ -483,6 +529,23 @@ struct BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle_FIFO
c_grid_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma);
}
// transposed WMMA output C' = B' * A'
__host__ __device__ static constexpr auto
GetCBlockDescriptor_MRepeat_MWave_MThreadPerSubGroup_NRepeat_NWave_NSubGroup_NAccVgprs()
{
constexpr auto c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma =
make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
Number<MWaves>{},
Number<MPerWMMA>{},
Number<NRepeat>{},
Number<NWaves>{},
Number<NPerWMMA>{}));
return wmma_gemm
.MakeCDesc_MBlockxRepeat_MWave_MThreadPerSubGroup_NBlockxRepeat_NWave_NSubGroup_NAccVgprs(
c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma);
}
// Provide dimension size
__host__ __device__ static constexpr auto
GetCBlockDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs()
......
include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp
View file @ 5df713ef
......@@ -1313,8 +1313,8 @@ template <typename SrcData,
typename DimAccessOrder,
index_t DstVectorDim,
index_t DstScalarPerVector,
index_t LowEightRowlaneIdx,
index_t HighEightRowLaneIdx,
uint32_t LowEightRowlaneIdx,
uint32_t HighEightRowLaneIdx,
typename enable_if<SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
bool>::type = false>
struct ThreadwiseTensorSliceTransfer_StaticToStatic_InterRow
......
include/ck/tensor_operation/gpu/warp/wmma_gemm.hpp
View file @ 5df713ef
......@@ -369,7 +369,7 @@ struct WmmaGemm
static constexpr auto I5 = Number<5>{};
using CIndex = MultiIndex<2>;
using CIndex4D = MultiIndex<4>;
using CIndex3D = MultiIndex<3>;
__host__ __device__ constexpr WmmaGemm()
{
......@@ -421,6 +421,46 @@ struct WmmaGemm
Sequence<5>{}));
}
// Transposed WMMA Output C' = B' * A'
template <typename CDesc_MBlockxRepeat_MWave_MPerWMMA_NBlockxRepeat_NWave_NPerWMMA>
__host__ __device__ static constexpr auto
MakeCDesc_MBlockxRepeat_MWave_MThreadPerSubGroup_NBlockxRepeat_NWave_NSubGroup_NAccVgprs(
const CDesc_MBlockxRepeat_MWave_MPerWMMA_NBlockxRepeat_NWave_NPerWMMA&
c_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma)
{
const auto MBlockxRepeat =
c_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma.GetLength(I0);
const auto NBlockxRepeat =
c_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma.GetLength(I3);
const auto MWave =
c_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma.GetLength(I1);
const auto NWave =
c_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma.GetLength(I4);
return transform_tensor_descriptor(
c_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma,
make_tuple(
make_pass_through_transform(MBlockxRepeat),
make_pass_through_transform(MWave),
make_pass_through_transform(Number<wmma_instr.num_thread_per_subgroups>{}),
make_pass_through_transform(NBlockxRepeat),
make_pass_through_transform(NWave),
make_unmerge_transform(make_tuple(Number<wmma_instr.num_subgroups>{},
Number<wmma_instr.num_acc_vgprs_per_wave>{}))),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5>{}),
make_tuple(Sequence<0>{},
Sequence<1>{},
Sequence<2>{},
Sequence<3>{},
Sequence<4>{},
Sequence<5, 6>{}));
}
__device__ static constexpr index_t GetRegSizePerWmma()
{
return wmma_instr.num_acc_vgprs_per_wave;
......@@ -493,6 +533,14 @@ struct WmmaGemm
return TransposeC ? CIndex{n_offset, m_offset} : CIndex{m_offset, n_offset};
}
__device__ static CIndex3D GetBeginOfThreadBlk3D()
{
index_t n_offset = GetLaneIdUnderSubGroup();
index_t m_offset = GetSubGroupId();
return TransposeC ? CIndex3D{n_offset, m_offset, I0} : CIndex3D{m_offset, n_offset, I0};
}
static constexpr auto wmma =
WmmaSelector<src_type_a, src_type_b, dst_type, MPerWmma, NPerWmma>{};
static constexpr auto wmma_instr = wmma.selected_wmma;
......
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