GQA-4 example

example/32_batched_gemm_scale_softmax_gemm/CMakeLists.txt

@@ -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)

example/32_batched_gemm_scale_softmax_gemm/grouped_query_attention_forward_wmma_fp16.cpp

+// 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); }

example/32_batched_gemm_scale_softmax_gemm/multi_query_attention_forward_wmma_fp16.cpp

@@ -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>

example/32_batched_gemm_scale_softmax_gemm/run_grouped_query_attention_forward_wmma.inc

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+int run(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+
+    // GEMM shape for A/B0/B1/C
+    // C_g_m_o = A_g_m_k * B0_g_k_n * B1_g_n_o
+    ck::index_t M = 1024;
+    ck::index_t N = 1024;
+    ck::index_t K = 64;
+    ck::index_t O = 64;
+
+    // Output shape C[G0, M, G1, O]. Batch dim, outer dim, inner dim must match GEMM shape
+    // C_g0_g1_m_o = reshape(C_g_m_o, [g0, g1, m, o])
+    // C_g0_m_g1_o = permute(C_g0_g1_m_o, [0, 2, 1, 3])
+    ck::index_t G0      = 4;
+    ck::index_t G1      = 16;
+    ck::index_t KV_head = QueryGroupNumber;
+
+    float alpha = 1;
+
+    bool input_permute  = false;
+    bool output_permute = true;
+
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 13)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M  = std::stoi(argv[4]);
+        N  = std::stoi(argv[5]);
+        K  = std::stoi(argv[6]);
+        O  = std::stoi(argv[7]);
+        G0 = std::stoi(argv[8]);
+        G1 = std::stoi(argv[9]);
+
+        alpha = std::stof(argv[10]);
+
+        input_permute  = std::stoi(argv[11]);
+        output_permute = std::stoi(argv[12]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=no, 1=yes)\n");
+        printf("arg4 to 11: M, N, K, O, G0, G1\n");
+        printf("arg10: scale (alpha)\n");
+        printf("arg11 to 12: input / output permute\n");
+        exit(0);
+    }
+
+    std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M, K};
+    std::vector<ck::index_t> a_gs_ms_ks_strides =
+        input_permute
+            ? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // A layout [G0, M, G1, K]
+            : std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // A layout [G0, G1, M, K]
+
+    std::vector<ck::index_t> b0_gs_ns_ks_lengths{G0, KV_head, N, K};
+    std::vector<ck::index_t> b0_gs_ns_ks_strides =
+        input_permute
+            ? std::vector<ck::index_t>{N * KV_head * K, K, KV_head * K, 1}
+            // B0 layout [G0, N, G1, K]
+            : std::vector<ck::index_t>{KV_head * N * K, N * K, K, 1}; // B0 layout [G0, G1, N, K]
+
+    std::vector<ck::index_t> b1_gs_os_ns_lengths{G0, KV_head, O, N};
+    std::vector<ck::index_t> b1_gs_os_ns_strides =
+        input_permute
+            ? std::vector<ck::index_t>{N * KV_head * O, O, 1, KV_head * O}
+            // B1 layout [G0, N, G1, O]
+            : std::vector<ck::index_t>{KV_head * N * O, N * O, 1, O}; // B1 layout [G0, G1, N, O]
+
+    std::vector<ck::index_t> c_gs_ms_os_lengths{G0, G1, M, O};
+    std::vector<ck::index_t> c_gs_ms_os_strides =
+        output_permute
+            ? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // C layout [G0, M, G1, O]
+            : std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // C layout [G0, G1, M, O]
+
+    Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
+    Tensor<B0DataType> b0_gs_ns_ks(b0_gs_ns_ks_lengths, b0_gs_ns_ks_strides);
+    Tensor<B1DataType> b1_gs_os_ns(b1_gs_os_ns_lengths, b1_gs_os_ns_strides);
+    Tensor<CDataType> c_gs_ms_os_host_result(c_gs_ms_os_lengths, c_gs_ms_os_strides);
+    Tensor<CDataType> c_gs_ms_os_device_result(c_gs_ms_os_lengths, c_gs_ms_os_strides);
+
+    std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
+    std::cout << "b0_gs_ns_ks: " << b0_gs_ns_ks.mDesc << std::endl;
+    std::cout << "b1_gs_os_ns: " << b1_gs_os_ns.mDesc << std::endl;
+    std::cout << "c_gs_ms_os: " << c_gs_ms_os_host_result.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    case 2:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<B0DataType>{0.0, 1.0});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_3<B1DataType>{-0.5, 0.5});
+        break;
+    case 3:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
+        break;
+    case 4: // A, B0, B1 1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 5: // Rand: b1 b0; unit: a
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    case 6: // Rand: a b0 ; unit: B1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 7: // Rand: a b1 ; unit: b0
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    case 8: // Rand: a ; unit: b0 b1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 9: // Rand: b0 ; unit: a b1
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<B1DataType>{});
+        break;
+    case 10: // Rand: b1 ; unit: a b0
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<B1DataType>{-2, 2});
+        break;
+    default:
+        a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_Sequential<2>{});
+        b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<B0DataType>{});
+        b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
+    }
+
+    DeviceMem a_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize());
+    DeviceMem b0_device_buf(sizeof(B0DataType) * b0_gs_ns_ks.mDesc.GetElementSpaceSize());
+    DeviceMem b1_device_buf(sizeof(B1DataType) * b1_gs_os_ns.mDesc.GetElementSpaceSize());
+    DeviceMem c_device_buf(sizeof(CDataType) *
+                           c_gs_ms_os_device_result.mDesc.GetElementSpaceSize());
+
+    a_device_buf.ToDevice(a_gs_ms_ks.mData.data());
+    b0_device_buf.ToDevice(b0_gs_ns_ks.mData.data());
+    b1_device_buf.ToDevice(b1_gs_os_ns.mData.data());
+
+    auto a_element_op    = AElementOp{};
+    auto b0_element_op   = B0ElementOp{};
+    auto acc0_element_op = Acc0ElementOp{alpha};
+    auto b1_element_op   = B1ElementOp{};
+    auto c_element_op    = CElementOp{};
+
+    // do GEMM
+    float best_perf         = .0;
+    float best_time         = .0;
+    int not_pass            = 0;
+    std::string best_kernel = "";
+    printf("Verification: %s\n", do_verification ? "ON" : "OFF");
+    // TODO ANT: replace array with vector?
+    ck::static_for<0, std::tuple_size_v<DeviceMHAFactory>, 1>{}([&](auto i) -> void {
+        const auto device_conv_mha_instance = std::get<i>(DeviceMHAFactory{});
+
+        using DeviceMHAInstance = ck::remove_cvref_t<decltype(device_conv_mha_instance)>;
+        auto gemm               = DeviceMHAInstance{};
+        auto invoker            = gemm.MakeInvoker();
+        auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
+                                          static_cast<B0DataType*>(b0_device_buf.GetDeviceBuffer()),
+                                          static_cast<B1DataType*>(b1_device_buf.GetDeviceBuffer()),
+                                          static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
+                                          M,
+                                          N,
+                                          K,
+                                          O,
+                                          G0,
+                                          G1,
+                                          alpha,
+                                          input_permute,
+                                          output_permute);
+
+        if(!gemm.IsSupportedArgument(argument))
+        {
+            std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+            // return 0;
+        }
+
+        float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+        std::size_t flop = (size_t(M) * N * K * 2 + size_t(M) * N * O * 2) * G0 * G1;
+        std::size_t num_btype =
+            (sizeof(ADataType) * M * K + sizeof(CDataType) * M * O) * G0 * G1 +
+            (sizeof(B0DataType) * K * N + sizeof(B1DataType) * N * O) * G0 * QueryGroupNumber;
+
+        float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << gemm.GetTypeString() << std::endl;
+        if(tflops > best_perf)
+        {
+            best_perf   = tflops;
+            best_time   = ave_time * 1000;
+            best_kernel = gemm.GetTypeString();
+        }
+        if(do_verification)
+        {
+            c_device_buf.FromDevice(c_gs_ms_os_device_result.mData.data());
+
+            Tensor<ADataType> a_g0_g1_m_k({G0, G1, M, K});
+            Tensor<B0DataType> b0_g0_gq_k_n({G0, QueryGroupNumber, K, N});
+            Tensor<B1DataType> b1_g0_gq_n_o({G0, QueryGroupNumber, N, O});
+            Tensor<Acc0DataType> acc0_g0_g1_m_n({G0, G1, M, N}); // scratch object after gemm0
+            Tensor<ADataType> a1_g0_g1_m_n({G0, G1, M, N});      // scratch object after softmax
+            Tensor<CDataType> c_g0_g1_m_o_host_result({G0, G1, M, O}); // scratch object after gemm1
+
+            // permute
+            a_gs_ms_ks.ForEach([&](auto& self, auto idx) {
+                a_g0_g1_m_k(idx[0], idx[1], idx[2], idx[3]) = self(idx);
+            });
+            b0_gs_ns_ks.ForEach([&](auto& self, auto idx) {
+                b0_g0_gq_k_n(idx[0], idx[1], idx[3], idx[2]) = self(idx);
+            });
+            b1_gs_os_ns.ForEach([&](auto& self, auto idx) {
+                b1_g0_gq_n_o(idx[0], idx[1], idx[3], idx[2]) = self(idx);
+            });
+
+            // gemm 0
+            auto ref_gemm0          = ReferenceGemm0Instance{};
+            auto ref_gemm0_invoker  = ref_gemm0.MakeInvoker();
+            auto ref_gemm0_argument = ref_gemm0.MakeArgument(a_g0_g1_m_k,
+                                                             b0_g0_gq_k_n,
+                                                             acc0_g0_g1_m_n,
+                                                             a_element_op,
+                                                             b0_element_op,
+                                                             acc0_element_op);
+
+            ref_gemm0_invoker.Run(ref_gemm0_argument);
+
+            // masking
+            const auto mask = typename DeviceMHAInstance::C0MatrixMask(N);
+            acc0_g0_g1_m_n.ForEach([&](auto& self, auto idx) {
+                if(mask.IsMaskedElement(idx[2], idx[3]))
+                    self(idx) = -ck::NumericLimits<float>::Infinity();
+            });
+
+            // softmax
+            auto ref_softmax         = ReferenceSoftmaxInstance{};
+            auto ref_softmax_invoker = ref_softmax.MakeInvoker();
+            auto ref_softmax_argument =
+                ref_softmax.MakeArgument(acc0_g0_g1_m_n, a1_g0_g1_m_n, 1, 0, {3});
+
+            ref_softmax_invoker.Run(ref_softmax_argument);
+
+            // gemm1
+            auto ref_gemm1          = ReferenceGemm1Instance{};
+            auto ref_gemm1_invoker  = ref_gemm1.MakeInvoker();
+            auto ref_gemm1_argument = ref_gemm1.MakeArgument(a1_g0_g1_m_n,
+                                                             b1_g0_gq_n_o,
+                                                             c_g0_g1_m_o_host_result,
+                                                             PassThrough{},
+                                                             b1_element_op,
+                                                             c_element_op);
+
+            ref_gemm1_invoker.Run(ref_gemm1_argument);
+
+            // permute
+            c_gs_ms_os_host_result.ForEach(
+                [&](auto& self, auto idx) { self(idx) = c_g0_g1_m_o_host_result(idx); });
+
+            // default absolute error and relative error is 0.001
+            double rtol = 1e-3;
+            double atol = 1e-3;
+
+            // when BF16 is taken, set absolute error and relative error to 0.01
+            if(std::is_same_v<ADataType, ck::bhalf_t> && std::is_same_v<B0DataType, ck::bhalf_t> &&
+               std::is_same_v<B1DataType, ck::bhalf_t> && std::is_same_v<CDataType, ck::bhalf_t>)
+            {
+                rtol = 1e-2;
+                atol = 1e-2;
+            }
+
+            bool this_run_verification = ck::utils::check_err(c_gs_ms_os_device_result.mData,
+                                                              c_gs_ms_os_host_result.mData,
+                                                              "Error: Incorrect results!",
+                                                              rtol,
+                                                              atol);
+            printf("Verification: %s, Pass: %s\n",
+                   do_verification ? "ON" : "OFF",
+                   this_run_verification ? "YES" : "NO");
+
+            if(!this_run_verification)
+            {
+                not_pass = 1;
+                printf("%d th MQA instance verification Failed \n", i.value);
+            }
+        }
+    });
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    std::cout << "Problem Size: BatchCount: " << G0 << ", HeadNum: " << G1 << ", M: " << M
+              << ", N: " << N << ", K: " << K << ", O: " << O << std::endl;
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    std::cout << "Best kernel: " << best_kernel << " , " << best_perf << " TFlops , " << best_time
+              << " us" << std::endl;
+    std::cout << "---------------------------------------------------------------------------------"
+                 "-----------"
+              << std::endl;
+    return not_pass;
+}

library/include/ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp

@@ -255,6 +255,130 @@ struct ReferenceBatchedGemm_MQA : public device::BaseOperator
     }
 };
 
+template <typename ADataType,
+          typename BDataType,
+          typename CDataType,
+          typename AccDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          ck::index_t QueryGroupNumber>
+struct ReferenceBatchedGemm_GQA : public device::BaseOperator
+{
+    // Argument
+    struct Argument : public device::BaseArgument
+    {
+        Argument(const Tensor<ADataType>& a_g0_g1_m_k,
+                 const Tensor<BDataType>& b_g0_gq_k_n,
+                 Tensor<CDataType>& c_g0_g1_m_n,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CElementwiseOperation c_element_op)
+            : a_g0_g1_m_k_{a_g0_g1_m_k},
+              b_g0_gq_k_n_{b_g0_gq_k_n},
+              c_g0_g1_m_n_{c_g0_g1_m_n},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              c_element_op_{c_element_op}
+        {
+        }
+
+        const Tensor<ADataType>& a_g0_g1_m_k_;
+        const Tensor<BDataType>& b_g0_gq_k_n_;
+        Tensor<CDataType>& c_g0_g1_m_n_;
+
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CElementwiseOperation c_element_op_;
+    };
+
+    // Invoker
+    struct Invoker : public device::BaseInvoker
+    {
+        using Argument = ReferenceBatchedGemm_GQA::Argument;
+
+        float Run(const Argument& arg)
+        {
+            auto f_g0g1mk_g0gqkn_g0g1mn = [&](auto g0, auto g1, auto m, auto n) {
+                const int G1 = arg.a_g0_g1_m_k_.mDesc.GetLengths()[1];
+                const int K  = arg.a_g0_g1_m_k_.mDesc.GetLengths()[3];
+
+                AccDataType v_acc = 0;
+
+                for(int k = 0; k < K; ++k)
+                {
+                    ADataType v_a;
+                    BDataType v_b;
+
+                    arg.a_element_op_(v_a, arg.a_g0_g1_m_k_(g0, g1, m, k));
+                    arg.b_element_op_(v_b, arg.b_g0_gq_k_n_(g0, g1 * QueryGroupNumber / G1, k, n));
+
+                    v_acc +=
+                        ck::type_convert<AccDataType>(v_a) * ck::type_convert<AccDataType>(v_b);
+                }
+
+                AccDataType v_c;
+
+                arg.c_element_op_(v_c, v_acc);
+
+                arg.c_g0_g1_m_n_(g0, g1, m, n) = ck::type_convert<CDataType>(v_c);
+            };
+
+            make_ParallelTensorFunctor(f_g0g1mk_g0gqkn_g0g1mn,
+                                       arg.c_g0_g1_m_n_.mDesc.GetLengths()[0],
+                                       arg.c_g0_g1_m_n_.mDesc.GetLengths()[1],
+                                       arg.c_g0_g1_m_n_.mDesc.GetLengths()[2],
+                                       arg.c_g0_g1_m_n_.mDesc.GetLengths()[3])(
+                std::thread::hardware_concurrency());
+            return 0;
+        }
+
+        float Run(const device::BaseArgument* p_arg,
+                  const StreamConfig& /* stream_config */ = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg));
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
+
+    static auto MakeArgument(const Tensor<ADataType>& a_g0_g1_m_k,
+                             const Tensor<BDataType>& b_g0_gq_k_n,
+                             Tensor<CDataType>& c_g0_g1_m_n,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CElementwiseOperation c_element_op)
+    {
+        return Argument{
+            a_g0_g1_m_k, b_g0_gq_k_n, c_g0_g1_m_n, a_element_op, b_element_op, c_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "ReferenceBatchedGemm_GQA"
+            << std::endl;
+        // clang-format on
+
+        return str.str();
+    }
+};
+
 } // namespace host
 } // namespace tensor_operation
 } // namespace ck