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GQA-4 example #833

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1 change: 1 addition & 0 deletions example/32_batched_gemm_scale_softmax_gemm/CMakeLists.txt
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Expand Up @@ -11,6 +11,7 @@ if(GPU_TARGETS MATCHES "gfx1100" OR GPU_TARGETS MATCHES "gfx1101" OR GPU_TARGETS
add_example_executable(example_self_attention_forward_wmma_fp16 self_attention_forward_wmma_fp16.cpp)
add_example_executable(example_cross_attention_forward_wmma_fp16 cross_attention_forward_wmma_fp16.cpp)
add_example_executable(example_multi_query_attention_forward_wmma_fp16 multi_query_attention_forward_wmma_fp16.cpp)
add_example_executable(example_grouped_query_attention_forward_wmma_fp16 grouped_query_attention_forward_wmma_fp16.cpp)
endif()

add_custom_target(example_gemm_scale_softmax_gemm)
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302 changes: 302 additions & 0 deletions example/32_batched_gemm_scale_softmax_gemm/grouped_query_attention_forward_wmma_fp16.cpp
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// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.

/*
Grouped Query Attention,
Ainslie, Joshua, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit
Sanghai. “GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.”
arXiv, May 22, 2023. https://doi.org/10.48550/arXiv.2305.13245.

Example is GQA-4
*/

#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>

#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_query_attention_forward_wmma.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"

#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"

template <ck::index_t... Is>
using S = ck::Sequence<Is...>;

using F16 = ck::half_t;
using F32 = float;

using PassThrough = ck::tensor_operation::element_wise::PassThrough;

using ADataType = F16;
using B0DataType = F16;
using B1DataType = F16;
using Acc0DataType = F32;
using Acc1DataType = F32;
using CShuffleDataType = F32;
using CDataType = F16;
using Acc0BiasDataType = ck::Tuple<>;
using Acc1BiasDataType = ck::Tuple<>;

static constexpr ck::index_t NumDimG = 2;
static constexpr ck::index_t NumDimM = 1;
static constexpr ck::index_t NumDimN = 1;
static constexpr ck::index_t NumDimK = 1;
static constexpr ck::index_t NumDimO = 1;
static constexpr ck::index_t QueryGroupNumber = 4;

using AElementOp = PassThrough;
using B0ElementOp = PassThrough;
using Acc0ElementOp = ck::tensor_operation::element_wise::Scale;
using B1ElementOp = PassThrough;
using CElementOp = PassThrough;

static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKOPadding;
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;

static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;

// clang-format off
// #define CK_MHA_USE_WAVE_1
// #define CK_MHA_USE_WAVE_2
// #define CK_MHA_USE_WAVE_4
#define CK_MHA_USE_WAVE_8
using DeviceMHAFactory =
std::tuple<
#ifdef CK_MHA_USE_WAVE_1
// 1 wave, mrepeat = 1, nrepeat = 2, k/o repeat = 1~5
ck::tensor_operation::device::DeviceGroupedQueryAttentionForward_Wmma<
NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
QueryGroupNumber,
32,
// Gemm 0
16, 128, 64, 8, 8,
// Gemm 1
64, 64, 8,
16, 16, 16,
// Per repeat = wave_m = wave_num, wave_n = 1
1, 8, 4,
// ABlockTransfer MK -> K0 M K1
S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B0BlockTransfer LK -> K0 L K1
S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B1BlockTransfer NL -> L0 N L1
S<2, 2, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 1, false,
// CShuffleBlockTransfer MN
1, 1, S<1, 16, 1, 2>, 8,
MaskingSpec>,
ck::tensor_operation::device::DeviceGroupedQueryAttentionForward_Wmma<
NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
QueryGroupNumber,
32,
// Gemm 0
16, 64, 64, 8, 8,
// Gemm 1
64, 64, 8,
16, 16, 16,
// Per repeat = wave_m = wave_num, wave_n = 1
1, 4, 4,
// ABlockTransfer MK -> K0 M K1
S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B0BlockTransfer LK -> K0 L K1
S<2, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B1BlockTransfer NL -> L0 N L1
S<2, 2, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 1, false,
// CShuffleBlockTransfer MN
1, 1, S<1, 16, 1, 2>, 8,
MaskingSpec>,
#endif
#ifdef CK_MHA_USE_WAVE_2
ck::tensor_operation::device::DeviceGroupedQueryAttentionForward_Wmma<
NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
QueryGroupNumber,
64,
// Gemm 0
32, 128, 64, 8, 8,
// Gemm 1
64, 64, 8,
16, 16, 16,
// Per repeat = wave_m = wave_num, wave_n = 1
1, 8, 4,
// ABlockTransfer MK -> K0 M K1
S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B0BlockTransfer LK -> K0 L K1
S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B1BlockTransfer NL -> L0 N L1
S<2, 4, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, false,
// CShuffleBlockTransfer MN
1, 1, S<1, 32, 1, 2>, 8,
MaskingSpec>,
ck::tensor_operation::device::DeviceGroupedQueryAttentionForward_Wmma<
NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
QueryGroupNumber,
64,
// Gemm 0
32, 64, 64, 8, 8,
// Gemm 1
64, 64, 8,
16, 16, 16,
// Per repeat = wave_m = wave_num, wave_n = 1
1, 4, 4,
// ABlockTransfer MK -> K0 M K1
S<2, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B0BlockTransfer LK -> K0 L K1
S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B1BlockTransfer NL -> L0 N L1
S<2, 4, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 1, false,
// CShuffleBlockTransfer MN
1, 1, S<1, 32, 1, 2>, 8,
MaskingSpec>,
#endif
#ifdef CK_MHA_USE_WAVE_4
ck::tensor_operation::device::DeviceGroupedQueryAttentionForward_Wmma<
NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
QueryGroupNumber,
128,
// Gemm 0
64, 128, 64, 8, 8,
// Gemm 1
64, 64, 8,
16, 16, 16,
// Per repeat = wave_m = wave_num, wave_n = 1
1, 8, 4,
// ABlockTransfer MK -> K0 M K1
S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B0BlockTransfer LK -> K0 L K1
S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B1BlockTransfer NL -> L0 N L1
S<2, 8, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 1, false,
// CShuffleBlockTransfer MN
1, 1, S<1, 64, 1, 2>, 8,
MaskingSpec>,
ck::tensor_operation::device::DeviceGroupedQueryAttentionForward_Wmma<
NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
QueryGroupNumber,
128,
// Gemm 0
64, 64, 64, 8, 8,
// Gemm 1
64, 64, 8,
16, 16, 16,
// Per repeat = wave_m = wave_num, wave_n = 1
1, 4, 4,
// ABlockTransfer MK -> K0 M K1
S<2, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B0BlockTransfer LK -> K0 L K1
S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B1BlockTransfer NL -> L0 N L1
S<2, 8, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 1, false,
// CShuffleBlockTransfer MN
1, 1, S<1, 64, 1, 2>, 8,
MaskingSpec>,
#endif
#ifdef CK_MHA_USE_WAVE_8
ck::tensor_operation::device::DeviceGroupedQueryAttentionForward_Wmma<
NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
QueryGroupNumber,
256,
// Gemm 0
128, 128, 64, 8, 8,
// Gemm 1
64, 64, 8,
16, 16, 16,
// Per repeat = wave_m = wave_num, wave_n = 1
1, 8, 4,
// ABlockTransfer MK -> K0 M K1
S<2, 128, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B0BlockTransfer LK -> K0 L K1
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B1BlockTransfer NL -> L0 N L1
S<2, 16, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, false,
// CShuffleBlockTransfer MN
1, 1, S<1, 128, 1, 2>, 8,
MaskingSpec>,
ck::tensor_operation::device::DeviceGroupedQueryAttentionForward_Wmma<
NumDimG, NumDimM, NumDimN, NumDimK, NumDimO,
ADataType, B0DataType, B1DataType, CDataType, Acc0BiasDataType, Acc0DataType, Acc1BiasDataType, Acc1DataType, CShuffleDataType,
AElementOp, B0ElementOp, Acc0ElementOp, B1ElementOp, CElementOp,
GemmSpec, TensorSpecA, TensorSpecB0, TensorSpecB1, TensorSpecC, 1,
QueryGroupNumber,
256,
// Gemm 0
128, 128, 64, 8, 8,
// Gemm 1
64, 64, 8,
16, 16, 16,
// Per repeat = wave_m = wave_num, wave_n = 1
1, 8, 4,
// ABlockTransfer MK -> K0 M K1
S<2, 128, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B0BlockTransfer LK -> K0 L K1
S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true,
// B1BlockTransfer NL -> L0 N L1
S<2, 16, 8>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 1, false,
// CShuffleBlockTransfer MN
1, 1, S<1, 128, 1, 2>, 8,
MaskingSpec>
#endif
>;
// clang-format on
// Ref Gemm0: fp16 in, fp32 out
using ReferenceGemm0Instance =
ck::tensor_operation::host::ReferenceBatchedGemm_GQA<ADataType,
B0DataType,
Acc0DataType,
Acc1DataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
QueryGroupNumber>;

// Ref Softmax: fp32 in, fp16 out
using ReferenceSoftmaxInstance =
ck::tensor_operation::host::ReferenceSoftmax<Acc0DataType, ADataType, Acc0DataType>;

// Ref Gemm1: fp16 in, fp16 out
using ReferenceGemm1Instance =
ck::tensor_operation::host::ReferenceBatchedGemm_GQA<ADataType,
B1DataType,
CDataType,
Acc1DataType,
AElementOp,
B1ElementOp,
CElementOp,
QueryGroupNumber>;

#include "run_grouped_query_attention_forward_wmma.inc"

int main(int argc, char* argv[]) { return run(argc, argv); }
9 changes: 4 additions & 5 deletions example/32_batched_gemm_scale_softmax_gemm/multi_query_attention_forward_wmma_fp16.cpp
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Expand Up @@ -2,11 +2,10 @@
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.

/*
Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g_k_l) * B1_g_l_n
|-----------------|
Gemm0
|-------------------------------------|
Gemm1
Multi-Query Attention
Shazeer, Noam. “Fast Transformer Decoding: One Write-Head Is All You Need.” arXiv.org, November 6,
2019. https://arxiv.org/abs/1911.02150v1.

*/

#include <iostream>
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