bwd hd256

example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_backward_v2.cpp

@@ -25,7 +25,7 @@ Kernel outputs:
 
 #define PRINT_HOST 0
 #define USING_MASK 0
-#define DIM 128 // DIM should be a multiple of 8.
+#define DIM 256 // DIM should be a multiple of 8.
 
 #include <iostream>
 #include <numeric>
@@ -38,6 +38,7 @@ Kernel outputs:
 #include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_batched_mha_bwd_xdl_cshuffle_qloop_v1.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_batched_mha_bwd_xdl_cshuffle_qloop_v2.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_batched_mha_bwd_xdl_cshuffle_qloop_v3.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 #include "ck/library/utility/check_err.hpp"
@@ -150,6 +151,19 @@ using DeviceGemmInstance =
     // ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V2<  NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, OutputDataType, GemmDataType, ZDataType, LSEDataType, Acc0BiasDataType, Acc1BiasDataType, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp,       Scale, QKVElementOp,  YElementOp,       GemmSpec,   TensorSpecQ,   TensorSpecK,   TensorSpecV,   TensorSpecY,        1,   256,     32,   128,   128,   128,    32,    64,   8,   8,    2,   32,   32,     1,     1,     4,     1,     S<8, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,      S<4, 64, 1>,      S<1, 0, 2>,      S<1, 0, 2>,               2,               8,               8,       true,               4,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           4,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;               
     // ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V2<  NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, OutputDataType, GemmDataType, ZDataType, LSEDataType, Acc0BiasDataType, Acc1BiasDataType, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp,       Scale, QKVElementOp,  YElementOp,       GemmSpec,   TensorSpecQ,   TensorSpecK,   TensorSpecV,   TensorSpecY,        1,   256,     32,   128,    64,   128,    32,    64,   8,   8,    2,   32,   32,     1,     1,     4,     1,     S<8, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,      S<4, 64, 1>,      S<1, 0, 2>,      S<1, 0, 2>,               2,               8,               8,       true,               4,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           4,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;
 // clang-format on
+#elif(DIM <= 256)
+// clang-format off
+using DeviceGemmInstance =
+    // ########################################################################################|  NumDimG| NumDimM| NumDimN| NumDimK| NumDimO| InputDataType| OutputDataType| GemmDataType| ZDataType| LSEDataType| Acc0BiasDataType| Acc1BiasDataType|     GemmAcc|        CShuffle|            A|            B|         Acc|           B1|           C|           GEMM|   ATensorSpec|  B0TensorSpec|  B1TensorSpec|   CTensorSpec| NumGemmK| Block| Gemm01| Gemm0| Gemm0| Gemm1| Gemm1| Gemm2| AK1| BK1| B1K1| MPer| NPer| Gemm0| Gemm0| Gemm1| Gemm2|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockTransfer| B0BlockLds| D0BlockTransfer|  B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockTransfer| B1BlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths| CShuffleBlockTransferScalarPerVector_NPerBlock| MaskingSpec| Deterministic|
+    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |    DataType|        DataType|  Elementwise|  Elementwise| Elementwise|  Elementwise| Elementwise| Specialization|              |              |              |              | Prefetch|  Size|   MPer|  NPer|  KPer|  NPer|  KPer|  KPer|    |    |     |  XDL|  XDL|  MXdl|  NXdl|  NXdl|  NXdl|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN|       SrcScalar|    ThreadCluster|   ThreadCluster|  SrcAccessOrder|    SrcVectorDim|       SrcScalar|       DstScalar|  AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl|                                               |            |              |
+    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |    Operation|    Operation|   Operation|    Operation|   Operation|               |              |              |              |              |    Stage|      |  Block| Block| Block| Block| Block| Block|    |    |     |     |     |   Per|   Per|   Per|   Per| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          |  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |       PerVector|  Lengths_K0_N_K1|    ArrangeOrder|                |                |       PerVector|    PerVector_K1|           |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|                                               |            |              |
+    // ########################################################################################|         |        |        |        |        |              |               |             |          |            |                 |                 |            |                |             |             |            |             |            |               |              |              |              |              |         |      |       |      |      |      |      |      |    |    |     |     |     |  Wave|  Wave|  Wave|  Wave|                |               |               |               |               |               |          |                 |                |                |                |                |                |           |                |                 |                |                |                |                |                |           |            |            |                             |                                               |            |              |
+    // ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V2<  NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, OutputDataType, GemmDataType, ZDataType, LSEDataType, Acc0BiasDataType, Acc1BiasDataType, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp,       Scale, QKVElementOp,  YElementOp,       GemmSpec,   TensorSpecQ,   TensorSpecK,   TensorSpecV,   TensorSpecY,        1,   256,     32,   128,   128,   128,    32,    64,   8,   8,    2,   32,   32,     1,     1,     4,     1,     S<8, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,      S<4, 64, 1>,      S<1, 0, 2>,      S<1, 0, 2>,               2,               8,               8,       true,               4,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           4,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;               
+    // ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V2<  NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, OutputDataType, GemmDataType, ZDataType, LSEDataType, Acc0BiasDataType, Acc1BiasDataType, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp,       Scale, QKVElementOp,  YElementOp,       GemmSpec,   TensorSpecQ,   TensorSpecK,   TensorSpecV,   TensorSpecY,        1,   256,     32,   128,    64,   128,    32,    64,   8,   8,    2,   32,   32,     1,     1,     4,     1,     S<8, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,      S<4, 64, 1>,      S<1, 0, 2>,      S<1, 0, 2>,               2,               8,               8,       true,               4,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           4,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>;
+    // ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V3<  NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, OutputDataType, GemmDataType, ZDataType, LSEDataType, Acc0BiasDataType, Acc1BiasDataType, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp,       Scale, QKVElementOp,  YElementOp,       GemmSpec,   TensorSpecQ,   TensorSpecK,   TensorSpecV,   TensorSpecY,        1,   256,     32,   128,    32,   256,    32,    64,   8,   8,    2,   32,   32,     1,     1,     8,     2,     S<8, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,      S<4, 64, 1>,      S<1, 0, 2>,      S<1, 0, 2>,               2,               8,               8,       true,               4,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           4,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>; 
+    ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V3<  NumDimG, NumDimM, NumDimN, NumDimK, NumDimO, InputDataType, OutputDataType, GemmDataType, ZDataType, LSEDataType, Acc0BiasDataType, Acc1BiasDataType, AccDataType, ShuffleDataType, QKVElementOp, QKVElementOp,       Scale, QKVElementOp,  YElementOp,       GemmSpec,   TensorSpecQ,   TensorSpecK,   TensorSpecV,   TensorSpecY,        1,   256,     32,   128,    32,   256,    32,    64,   8,   8,    2,   32,   32,     1,     1,     8,     2,     S<8, 32, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              4,              4,      true,      S<4, 64, 1>,      S<1, 0, 2>,      S<1, 0, 2>,               2,               8,               8,       true,               4,      S<8, 32, 1>,      S<0, 2, 1>,      S<0, 2, 1>,               1,               4,               2,      false,           1,           4,               S<1, 32, 1, 8>, CShuffleBlockTransferScalarPerVector_NPerBlock, MaskingSpec, Deterministic>; 
+                  
+// clang-format on
 #endif
 
 // Ref Gemm0: S = alpha * Q * K^T

include/ck/tensor_operation/gpu/device/impl/device_batched_mha_bwd_xdl_cshuffle_qloop_v3.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+#include <numeric>
+
+#include "ck/utility/common_header.hpp"
+#include "ck/utility/philox_rand.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/masking_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_batched_mha_bwd_xdl_cshuffle_qloop_b2t_v3.hpp"
+#include "ck/tensor_operation/operator_transform/transform_contraction_to_gemm.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename GridwiseGemm,
+          typename InputDataType,
+          typename D0DataType,
+          typename OutputDataType,
+          typename ZDataType,
+          typename LSEDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename AccElementwiseOperation,
+          typename B1ElementwiseOperation,
+          typename CElementwiseOperation,
+          typename AGridDesc_AK0_M_AK1,
+          typename BGridDesc_BK0_N_BK1,
+          typename D0GridDescriptor_M0_N0_M1_M2_N1_M3,
+          typename ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3,
+          typename B1GridDesc_BK0_N_BK1,
+          typename YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock,
+          typename LSEGridDescriptor_M,
+          typename YGradGridDesc_M0_O_M1,
+          typename Block2CTileMap,
+          typename ComputeBasePtrOfStridedBatch,
+          typename C0MatrixMask,
+          bool HasMainKBlockLoop,
+          bool IsDropout,
+          bool Deterministic>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, /*CK_MIN_BLOCK_PER_CU*/ 1)
+#endif
+        kernel_batched_multihead_attention_backward_qloop_xdl_cshuffle_v3(
+            const InputDataType* __restrict__ p_a_grid,
+            const InputDataType* __restrict__ p_b_grid,
+            const D0DataType* __restrict__ p_d0_grid,
+            ZDataType* __restrict__ p_z_grid,
+            const InputDataType* __restrict__ p_b1_grid,
+            const InputDataType* __restrict__ p_c_grid,
+            const LSEDataType* __restrict__ p_lse_grid,
+            const InputDataType* __restrict__ p_ygrad_grid,
+            OutputDataType* __restrict__ p_qgrad_grid,
+            OutputDataType* __restrict__ p_kgrad_grid,
+            D0DataType* __restrict__ p_d0grad_grid,
+            OutputDataType* __restrict__ p_vgrad_grid,
+            const AElementwiseOperation a_element_op,
+            const BElementwiseOperation b_element_op,
+            const AccElementwiseOperation acc_element_op,
+            const B1ElementwiseOperation b1_element_op,
+            const CElementwiseOperation c_element_op,
+            const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
+            const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
+            const BGridDesc_BK0_N_BK1 bgrad_grid_desc_bk0_n_bk1,
+            const D0GridDescriptor_M0_N0_M1_M2_N1_M3 d0_grid_desc_m0_n0_m1_m2_n1_m3,
+            const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
+                c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+            const B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1,
+            const B1GridDesc_BK0_N_BK1 b1grad_grid_desc_bk0_n_bk1,
+            const YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+            const LSEGridDescriptor_M lse_grid_desc_m,
+            const YGradGridDesc_M0_O_M1 ygrad_grid_desc_m0_o_m1,
+            const Block2CTileMap block_2_ctile_map,
+            const index_t batch_count,
+            const index_t h_ratio,
+            const index_t nblock,
+            const ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch,
+            const C0MatrixMask c0_matrix_mask,
+            const float p_drop,
+            const unsigned long long seed,
+            const unsigned long long offset,
+            const index_t raw_m_padded,
+            const index_t raw_n_padded)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
+    defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+    const index_t num_blocks_per_batch =
+        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
+    const index_t gkv_idx = __builtin_amdgcn_readfirstlane(g_idx / h_ratio);
+
+    // NOTE: assumes QKVY has the same layout as dQ/dK/dV/dY therefore being able to reuse batch
+    // offsets
+    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetABasePtr(g_idx)));
+    const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetBBasePtr(gkv_idx)));
+    const long_index_t z_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetZBasePtr(g_idx)));
+    const long_index_t b1_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1BasePtr(gkv_idx)));
+    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetCBasePtr(g_idx)));
+    const long_index_t lse_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetLSEBasePtr(g_idx)));
+    const long_index_t bgrad_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetBGradBasePtr(g_idx)));
+    const long_index_t b1grad_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_base_ptr_of_batch.GetB1GradBasePtr(g_idx)));
+
+    ck::philox ph(seed, 0, offset);
+    ZDataType* z_matrix_ptr = (p_z_grid == nullptr ? nullptr : p_z_grid + z_batch_offset);
+
+    const index_t z_random_matrix_offset = g_idx * raw_m_padded * raw_n_padded;
+
+    const D0DataType* tmp_p_d0_grid = nullptr;
+    D0DataType* tmp_p_d0grad_grid   = nullptr;
+    if constexpr(!is_same<D0DataType, void>::value)
+    {
+        const long_index_t d0_batch_offset = __builtin_amdgcn_readfirstlane(
+            static_cast<long_index_t>(compute_base_ptr_of_batch.GetD0BasePtr(g_idx)));
+        if(p_d0_grid != nullptr)
+        {
+            tmp_p_d0_grid = p_d0_grid + d0_batch_offset;
+        }
+        if(p_d0grad_grid != nullptr)
+        {
+            tmp_p_d0grad_grid = p_d0grad_grid + d0_batch_offset;
+        }
+    }
+
+    if constexpr(Deterministic)
+    {
+        for(index_t i = 0; i < nblock; i++)
+        {
+            GridwiseGemm::template Run<HasMainKBlockLoop, IsDropout>(
+                p_a_grid + a_batch_offset,
+                p_b_grid + b_batch_offset,
+                tmp_p_d0_grid,
+                z_matrix_ptr,
+                p_b1_grid + b1_batch_offset,
+                p_c_grid + c_batch_offset,
+                p_lse_grid + lse_batch_offset,
+                p_ygrad_grid + c_batch_offset,
+                p_qgrad_grid + a_batch_offset,
+                p_kgrad_grid + bgrad_batch_offset,
+                tmp_p_d0grad_grid,
+                p_vgrad_grid + b1grad_batch_offset,
+                p_shared,
+                a_element_op,
+                b_element_op,
+                acc_element_op,
+                b1_element_op,
+                c_element_op,
+                a_grid_desc_ak0_m_ak1,
+                b_grid_desc_bk0_n_bk1,
+                bgrad_grid_desc_bk0_n_bk1,
+                d0_grid_desc_m0_n0_m1_m2_n1_m3,
+                c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                b1_grid_desc_bk0_n_bk1,
+                b1grad_grid_desc_bk0_n_bk1,
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+                lse_grid_desc_m,
+                ygrad_grid_desc_m0_o_m1,
+                block_2_ctile_map,
+                c0_matrix_mask,
+                p_drop,
+                ph,
+                z_random_matrix_offset,
+                raw_n_padded,
+                i);
+        }
+    }
+    else
+    {
+        GridwiseGemm::template Run<HasMainKBlockLoop, IsDropout>(
+            p_a_grid + a_batch_offset,
+            p_b_grid + b_batch_offset,
+            tmp_p_d0_grid,
+            z_matrix_ptr,
+            p_b1_grid + b1_batch_offset,
+            p_c_grid + c_batch_offset,
+            p_lse_grid + lse_batch_offset,
+            p_ygrad_grid + c_batch_offset,
+            p_qgrad_grid + a_batch_offset,
+            p_kgrad_grid + bgrad_batch_offset,
+            tmp_p_d0grad_grid,
+            p_vgrad_grid + b1grad_batch_offset,
+            p_shared,
+            a_element_op,
+            b_element_op,
+            acc_element_op,
+            b1_element_op,
+            c_element_op,
+            a_grid_desc_ak0_m_ak1,
+            b_grid_desc_bk0_n_bk1,
+            bgrad_grid_desc_bk0_n_bk1,
+            d0_grid_desc_m0_n0_m1_m2_n1_m3,
+            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+            b1_grid_desc_bk0_n_bk1,
+            b1grad_grid_desc_bk0_n_bk1,
+            c_grid_desc_mblock_mperblock_nblock_nperblock,
+            lse_grid_desc_m,
+            ygrad_grid_desc_m0_o_m1,
+            block_2_ctile_map,
+            c0_matrix_mask,
+            p_drop,
+            ph,
+            z_random_matrix_offset,
+            raw_n_padded,
+            0);
+    }
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_d0_grid;
+    ignore = p_z_grid;
+    ignore = p_b1_grid;
+    ignore = p_c_grid;
+    ignore = p_lse_grid;
+    ignore = p_ygrad_grid;
+    ignore = p_qgrad_grid;
+    ignore = p_kgrad_grid;
+    ignore = p_d0grad_grid;
+    ignore = p_vgrad_grid;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = acc_element_op;
+    ignore = b1_element_op;
+    ignore = c_element_op;
+    ignore = a_grid_desc_ak0_m_ak1;
+    ignore = b_grid_desc_bk0_n_bk1;
+    ignore = bgrad_grid_desc_bk0_n_bk1;
+    ignore = d0_grid_desc_m0_n0_m1_m2_n1_m3;
+    ignore = c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3;
+    ignore = b1_grid_desc_bk0_n_bk1;
+    ignore = b1grad_grid_desc_bk0_n_bk1;
+    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = lse_grid_desc_m;
+    ignore = ygrad_grid_desc_m0_o_m1;
+    ignore = block_2_ctile_map;
+    ignore = batch_count;
+    ignore = h_ratio;
+    ignore = nblock;
+    ignore = compute_base_ptr_of_batch;
+    ignore = c0_matrix_mask;
+    ignore = p_drop;
+    ignore = seed;
+    ignore = offset;
+    ignore = raw_m_padded;
+    ignore = raw_n_padded;
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
+
+// Computes C = A * B0 * B1
+//              ^^^^^^ (Acc0)
+//              ^^^^^^^^^^^ (Acc1)
+template <index_t NumDimG,
+          index_t NumDimM,
+          index_t NumDimN,
+          index_t NumDimK,
+          index_t NumDimO, // NumDimGemm1N
+          typename InputDataType,
+          typename OutputDataType,
+          typename GemmDataType,
+          typename ZDataType,
+          typename LSEDataType,
+          typename Acc0BiasDataType,
+          typename Acc1BiasDataType,
+          typename GemmAccDataType,
+          typename CShuffleDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename AccElementwiseOperation,
+          typename B1ElementwiseOperation,
+          typename CElementwiseOperation,
+          GemmSpecialization GemmSpec,
+          TensorSpecialization ASpec,
+          TensorSpecialization BSpec,
+          TensorSpecialization B1Spec,
+          TensorSpecialization CSpec,
+          index_t NumGemmKPrefetchStage,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock, // Gemm0NPerBlock
+          index_t KPerBlock, // Gemm0KPerBlock
+          index_t Gemm1NPerBlock,
+          index_t Gemm1KPerBlock,
+          index_t Gemm2KPerBlock,
+          index_t AK1,
+          index_t BK1,
+          index_t B1K1,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          index_t Gemm1NXdlPerWave,
+          index_t Gemm2NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          bool ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          bool BBlockLdsExtraN,
+          index_t D0BlockTransferSrcScalarPerVector,
+          typename B1BlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename B1BlockTransferThreadClusterArrangeOrder,
+          typename B1BlockTransferSrcAccessOrder,
+          index_t B1BlockTransferSrcVectorDim,
+          index_t B1BlockTransferSrcScalarPerVector,
+          index_t B1BlockTransferDstScalarPerVector_BK1,
+          bool B1BlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+          MaskingSpecialization MaskingSpec,
+          bool Deterministic,
+          LoopScheduler LoopSched = LoopScheduler::Default>
+struct DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V3
+    : public BaseOperator // TODO inherit atten bwd op once API stablizes
+{
+    static_assert(NumDimG > 0 && NumDimM > 0 && NumDimN > 0 && NumDimK > 0 && NumDimO > 0,
+                  "Number of dimension must be greater than 0");
+
+    using D0DataType = Acc0BiasDataType;
+    using D1DataType = Acc1BiasDataType;
+
+    // TODO: implement bias combination
+    static_assert(std::is_void<D1DataType>::value, "Acc1 Bias addition is unimplemented");
+
+    using DeviceOp = DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V3;
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+
+    static constexpr index_t V_O1 = BK1;
+    static constexpr index_t Y_O1 = AK1;
+    static constexpr index_t Y_M1 = B1K1;
+
+    static constexpr auto padder = GemmGemmPadder<GemmSpec,
+                                                  Number<MPerBlock>,
+                                                  Number<NPerBlock>,
+                                                  Number<KPerBlock>,
+                                                  Number<Gemm1NPerBlock>>{};
+
+    using Transform = TransformBatchedContractionContractionToBatchedGemmGemm<
+        Sequence<NumDimG, NumDimM, NumDimN, NumDimK, NumDimO>,
+        Sequence<MPerBlock, NPerBlock, KPerBlock, Gemm1NPerBlock>,
+        GemmSpec,
+        ASpec,
+        BSpec,
+        B1Spec,
+        CSpec>;
+
+    /*
+    Descriptors for inputs:
+
+      Q, K, V, Y, dY, per-row softmax stats
+
+    Descriptors for outputs:
+
+      dQ, dK, dV
+
+    */
+
+    // Q in Gemm A position
+    static auto MakeAGridDescriptor_AK0_M_AK1(const std::vector<index_t>& a_gs_ms_ks_lengths,
+                                              const std::vector<index_t>& a_gs_ms_ks_strides)
+    {
+        return Transform::MakeAGridDescriptor_AK0_M_AK1(
+            Transform::MakeAGridDescriptor_M_K(a_gs_ms_ks_lengths, a_gs_ms_ks_strides),
+            Number<AK1>{});
+    }
+
+    // K in Gemm B0 position
+    static auto MakeBGridDescriptor_BK0_N_BK1(const std::vector<index_t>& b_gs_ns_ks_lengths,
+                                              const std::vector<index_t>& b_gs_ns_ks_strides)
+    {
+        return Transform::MakeB0GridDescriptor_BK0_N_BK1(
+            Transform::MakeB0GridDescriptor_N_K(b_gs_ns_ks_lengths, b_gs_ns_ks_strides),
+            Number<BK1>{});
+    }
+
+    // V in Gemm B1 position
+    static auto
+    MakeB1GridDescriptor_BK0_N_BK1(const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths,
+                                   const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides)
+    {
+        return Transform::MakeB1GridDescriptor_BK0_N_BK1(
+            Transform::MakeB1GridDescriptor_N_K(b1_gs_gemm1ns_gemm1ks_lengths,
+                                                b1_gs_gemm1ns_gemm1ks_strides),
+            Number<B1K1>{});
+    }
+
+    //
+    // dV = P^T * dY
+    //
+
+    // VGrad in Gemm C position
+    static auto MakeVGradGridDescriptor_N_O(const std::vector<index_t>& v_gs_os_ns_lengths,
+                                            const std::vector<index_t>& v_gs_os_ns_strides)
+    {
+        // v_gs_os_ns -> vgrad_gs_ns_os. O dims last because output is row-major.
+        // Here directly rearrange lengths/strides before constructing tensor descriptor to reduce
+        // transformation overhead
+        // TODO: This will be much easier when inputs are Gs, Ms, Ns, Os. So there's no need to
+        // extract subsequence and shuffle them.
+        const index_t num_dims = NumDimG + NumDimN + NumDimO;
+
+        // 0, 1, .. NumDimG - 1
+        std::vector<index_t> gs_ids(NumDimG);
+        std::iota(gs_ids.begin(), gs_ids.end(), 0);
+
+        // NumDimG, NumDimG + 1, ... NumDimG + NumDimO - 1
+        std::vector<index_t> os_ids(NumDimO);
+        std::iota(os_ids.begin(), os_ids.end(), NumDimG);
+
+        // NumDimG + NumDimO, NumDimG + NumDimO + 1, ... NumDimG + NumDimO + NumDimN - 1
+        std::vector<index_t> ns_ids(NumDimN);
+        std::iota(ns_ids.begin(), ns_ids.end(), NumDimG + NumDimO);
+
+        std::vector<index_t> ids_old2new;
+        ids_old2new.insert(ids_old2new.end(), gs_ids.begin(), gs_ids.end());
+        ids_old2new.insert(ids_old2new.end(), ns_ids.begin(), ns_ids.end());
+        ids_old2new.insert(ids_old2new.end(), os_ids.begin(), os_ids.end());
+
+        std::vector<index_t> v_gs_ns_os_lengths(num_dims), v_gs_ns_os_strides(num_dims);
+        for(int i = 0; i < num_dims; i++)
+        {
+            index_t id_new        = ids_old2new[i];
+            v_gs_ns_os_lengths[i] = v_gs_os_ns_lengths[id_new];
+            v_gs_ns_os_strides[i] = v_gs_os_ns_strides[id_new];
+        }
+
+        const auto vgrad_desc_nraw_oraw =
+            MakeGridDescriptorPair<NumDimG, NumDimN, NumDimO, TensorSpecialization::Default>(
+                v_gs_ns_os_lengths, v_gs_ns_os_strides)
+                .second;
+
+        return PadTensorDescriptor(vgrad_desc_nraw_oraw,
+                                   make_tuple(NPerBlock, Gemm1NPerBlock),
+                                   Sequence<padder.PadN, padder.PadO>{});
+    }
+
+    template <typename YGridDesc_M_O>
+    static auto MakeYGradGridDescriptor_M0_O_M1(const YGridDesc_M_O& ygrad_grid_desc_m_o)
+    {
+        const auto M = ygrad_grid_desc_m_o.GetLength(I0);
+        const auto O = ygrad_grid_desc_m_o.GetLength(I1);
+
+        const auto Y_M0 = M / Y_M1;
+
+        return transform_tensor_descriptor(
+            ygrad_grid_desc_m_o,
+            make_tuple(make_unmerge_transform(make_tuple(Y_M0, Y_M1)),
+                       make_pass_through_transform(O)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+    }
+
+    //
+    // dP = dY * V^T
+    //
+
+    // YGrad in Gemm A position
+    static auto MakeYGradGridDescriptor_O0_M_O1(const std::vector<index_t>& y_gs_ms_os_lengths,
+                                                const std::vector<index_t>& y_gs_ms_os_strides)
+    {
+        return Transform::MakeAGridDescriptor_AK0_M_AK1(
+            Transform::MakeAGridDescriptor_M_K(y_gs_ms_os_lengths, y_gs_ms_os_strides),
+            Number<Y_O1>{});
+    }
+
+    // V in Gemm B position
+    static auto MakeVGridDescriptor_O0_N_O1(const std::vector<index_t>& v_gs_os_ns_lengths,
+                                            const std::vector<index_t>& v_gs_os_ns_strides)
+    {
+        // v_gs_os_ns -> vgrad_gs_ns_os. O dims last because output is row-major.
+        // Here directly rearrange lengths/strides before constructing tensor descriptor to reduce
+        // transformation overhead
+        // TODO: This will be much easier when inputs are Gs, Ms, Ns, Os. So there's no need to
+        // extract subsequence and shuffle them.
+        const index_t num_dims = NumDimG + NumDimN + NumDimO;
+
+        // 0, 1, .. NumDimG - 1
+        std::vector<index_t> gs_ids(NumDimG);
+        std::iota(gs_ids.begin(), gs_ids.end(), 0);
+
+        // NumDimG, NumDimG + 1, ... NumDimG + NumDimO - 1
+        std::vector<index_t> os_ids(NumDimO);
+        std::iota(os_ids.begin(), os_ids.end(), NumDimG);
+
+        // NumDimG + NumDimO, NumDimG + NumDimO + 1, ... NumDimG + NumDimO + NumDimN - 1
+        std::vector<index_t> ns_ids(NumDimN);
+        std::iota(ns_ids.begin(), ns_ids.end(), NumDimG + NumDimO);
+
+        std::vector<index_t> ids_old2new;
+        ids_old2new.insert(ids_old2new.end(), gs_ids.begin(), gs_ids.end());
+        ids_old2new.insert(ids_old2new.end(), ns_ids.begin(), ns_ids.end());
+        ids_old2new.insert(ids_old2new.end(), os_ids.begin(), os_ids.end());
+
+        std::vector<index_t> v_gs_ns_os_lengths(num_dims), v_gs_ns_os_strides(num_dims);
+        for(int i = 0; i < num_dims; i++)
+        {
+            index_t id_new        = ids_old2new[i];
+            v_gs_ns_os_lengths[i] = v_gs_os_ns_lengths[id_new];
+            v_gs_ns_os_strides[i] = v_gs_os_ns_strides[id_new];
+        }
+
+        const auto v_grid_desc_nraw_oraw =
+            MakeGridDescriptorPair<NumDimG, NumDimN, NumDimO, TensorSpecialization::Default>(
+                v_gs_ns_os_lengths, v_gs_ns_os_strides)
+                .second;
+
+        const auto v_grid_desc_n_o = PadTensorDescriptor(v_grid_desc_nraw_oraw,
+                                                         make_tuple(NPerBlock, Gemm1NPerBlock),
+                                                         Sequence<padder.PadN, padder.PadO>{});
+
+        // N_O to O0_N_O1; to refactor
+        return Transform::MakeB0GridDescriptor_BK0_N_BK1(v_grid_desc_n_o, Number<V_O1>{});
+    }
+
+    // D0 in Gemm0 C position
+    static auto MakeD0GridDescriptor_M_N(const std::vector<index_t>& d_gs_ms_ns_lengths,
+                                         const std::vector<index_t>& d_gs_ms_ns_strides)
+    {
+        return Transform::MakeC0GridDescriptor_M_N(d_gs_ms_ns_lengths, d_gs_ms_ns_strides);
+    }
+
+    // Z in Gemm0 C position
+    static auto MakeZGridDescriptor_M_N(const std::vector<index_t>& z_gs_ms_ns_lengths,
+                                        const std::vector<index_t>& z_gs_ms_ns_strides)
+    {
+        return Transform::MakeC0GridDescriptor_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides);
+    }
+
+    static auto MakeLSEGridDescriptor_M(index_t MRaw)
+    {
+        const auto lse_grid_desc_mraw = make_naive_tensor_descriptor_packed(make_tuple(MRaw));
+
+        const auto M    = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
+        const auto MPad = M - MRaw;
+
+        if constexpr(GemmSpec == GemmSpecialization::MPadding ||
+                     GemmSpec == GemmSpecialization::MNPadding ||
+                     GemmSpec == GemmSpecialization::MKPadding ||
+                     GemmSpec == GemmSpecialization::MNKPadding)
+        {
+            // pad M
+            return transform_tensor_descriptor(lse_grid_desc_mraw,
+                                               make_tuple(make_right_pad_transform(MRaw, MPad)),
+                                               make_tuple(Sequence<0>{}),
+                                               make_tuple(Sequence<0>{}));
+        }
+        else
+        {
+            // not pad M
+            return lse_grid_desc_mraw;
+        }
+    }
+
+    using AGridDesc_AK0_M_AK1  = decltype(MakeAGridDescriptor_AK0_M_AK1({}, {}));
+    using BGridDesc_BK0_N_BK1  = decltype(MakeBGridDescriptor_BK0_N_BK1({}, {}));
+    using D0GridDesc_G_M_N     = decltype(Transform::MakeC0GridDescriptor_G_M_N({}, {}));
+    using B1GridDesc_BK0_N_BK1 = decltype(MakeB1GridDescriptor_BK0_N_BK1({}, {}));
+    using YGridDesc_M_O        = decltype(Transform::MakeCGridDescriptor_M_N({}, {}));
+    using LSEGridDesc_M        = decltype(MakeLSEGridDescriptor_M(1));
+    using AGridDesc_G_M_K      = decltype(Transform::MakeAGridDescriptor_G_M_K({}, {}));
+    using BGridDesc_G_N_K      = decltype(Transform::MakeB0GridDescriptor_G_N_K({}, {}));
+    using B1GridDesc_G_N_K     = decltype(Transform::MakeB1GridDescriptor_G_N_K({}, {}));
+    using CGridDesc_G_M_N      = decltype(Transform::MakeCGridDescriptor_G_M_N({}, {}));
+    using ZGridDesc_G_M_N      = decltype(Transform::MakeC0GridDescriptor_G_M_N({}, {}));
+
+    using D0GridDesc_M_N        = decltype(MakeD0GridDescriptor_M_N({}, {}));
+    using KGridDesc_N_K         = decltype(Transform::MakeB0GridDescriptor_N_K({}, {}));
+    using YGradGridDesc_M0_O_M1 = decltype(MakeYGradGridDescriptor_M0_O_M1(YGridDesc_M_O{}));
+    using ZGridDesc_M_N         = decltype(MakeZGridDescriptor_M_N({}, {}));
+
+    constexpr static auto make_MaskOutPredicate()
+    {
+        if constexpr(MaskingSpec == MaskingSpecialization::MaskDisabled)
+        {
+            return MaskDisabledPredicate{};
+        }
+        else if constexpr(MaskingSpec == MaskingSpecialization::MaskUpperTriangleFromTopLeft)
+        {
+            return MaskUpperTriangleFromTopLeftPredicate{};
+        }
+        else if constexpr(MaskingSpec == MaskingSpecialization::MaskUpperTriangleFromBottomRight)
+        {
+            return MaskUpperTriangleFromBottomRightPredicate{};
+        }
+    }
+    using C0MatrixMask = C0MatrixMask_impl<decltype(make_MaskOutPredicate())>;
+
+    struct ComputeBasePtrOfStridedBatch
+    {
+        ComputeBasePtrOfStridedBatch() {}
+        ComputeBasePtrOfStridedBatch(const AGridDesc_G_M_K& a_grid_desc_g_m_k,
+                                     const BGridDesc_G_N_K& b_grid_desc_g_n_k,
+                                     const D0GridDesc_G_M_N& d0_grid_desc_g_m_n,
+                                     const ZGridDesc_G_M_N& z_grid_desc_g_m_n,
+                                     const B1GridDesc_G_N_K& b1_grid_desc_g_n_k,
+                                     const CGridDesc_G_M_N& c_grid_desc_g_m_n,
+                                     const BGridDesc_G_N_K& bgrad_grid_desc_g_n_k,
+                                     const B1GridDesc_G_N_K& b1grad_grid_desc_g_n_k,
+                                     index_t BatchStrideLSE)
+            : a_grid_desc_g_m_k_(a_grid_desc_g_m_k),
+              b_grid_desc_g_n_k_(b_grid_desc_g_n_k),
+              d0_grid_desc_g_m_n_(d0_grid_desc_g_m_n),
+              z_grid_desc_g_m_n_(z_grid_desc_g_m_n),
+              b1_grid_desc_g_n_k_(b1_grid_desc_g_n_k),
+              c_grid_desc_g_m_n_(c_grid_desc_g_m_n),
+              bgrad_grid_desc_g_n_k_(bgrad_grid_desc_g_n_k),
+              b1grad_grid_desc_g_n_k_(b1grad_grid_desc_g_n_k),
+              BatchStrideLSE_(BatchStrideLSE)
+        {
+        }
+
+        __host__ __device__ constexpr long_index_t GetABasePtr(index_t g_idx) const
+        {
+            return a_grid_desc_g_m_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetBBasePtr(index_t g_idx) const
+        {
+            return b_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetD0BasePtr(index_t g_idx) const
+        {
+            return d0_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetZBasePtr(index_t g_idx) const
+        {
+            return z_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetB1BasePtr(index_t g_idx) const
+        {
+            return b1_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetCBasePtr(index_t g_idx) const
+        {
+            return c_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetLSEBasePtr(index_t g_idx) const
+        {
+            return g_idx * static_cast<long_index_t>(BatchStrideLSE_);
+        }
+
+        __host__ __device__ constexpr long_index_t GetBGradBasePtr(index_t g_idx) const
+        {
+            return bgrad_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        __host__ __device__ constexpr long_index_t GetB1GradBasePtr(index_t g_idx) const
+        {
+            return b1grad_grid_desc_g_n_k_.CalculateOffset(make_multi_index(g_idx, 0, 0));
+        }
+
+        private:
+        AGridDesc_G_M_K a_grid_desc_g_m_k_;
+        BGridDesc_G_N_K b_grid_desc_g_n_k_;
+        D0GridDesc_G_M_N d0_grid_desc_g_m_n_;
+        ZGridDesc_G_M_N z_grid_desc_g_m_n_;
+        B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
+        CGridDesc_G_M_N c_grid_desc_g_m_n_;
+        BGridDesc_G_N_K bgrad_grid_desc_g_n_k_;
+        B1GridDesc_G_N_K b1grad_grid_desc_g_n_k_;
+
+        index_t BatchStrideLSE_;
+    };
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V3<
+        InputDataType, // TODO: distinguish A/B datatype
+        D0DataType,
+        OutputDataType,
+        ZDataType,
+        GemmDataType,
+        GemmAccDataType,
+        CShuffleDataType,
+        LSEDataType,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        AccElementwiseOperation,
+        B1ElementwiseOperation,
+        CElementwiseOperation,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_AK0_M_AK1,
+        BGridDesc_BK0_N_BK1,
+        KGridDesc_N_K,
+        D0GridDesc_M_N,
+        ZGridDesc_M_N,
+        B1GridDesc_BK0_N_BK1,
+        YGridDesc_M_O,
+        LSEGridDesc_M,
+        NumGemmKPrefetchStage,
+        BlockSize,
+        MPerBlock,
+        NPerBlock,
+        KPerBlock,
+        Gemm1NPerBlock,
+        Gemm1KPerBlock,
+        Gemm2KPerBlock,
+        AK1,
+        BK1,
+        B1K1,
+        MPerXDL,
+        NPerXDL,
+        MXdlPerWave,
+        NXdlPerWave,
+        Gemm1NXdlPerWave,
+        Gemm2NXdlPerWave,
+        ABlockTransferThreadClusterLengths_AK0_M_AK1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_AK1,
+        true,
+        ABlockLdsExtraM,
+        BBlockTransferThreadClusterLengths_BK0_N_BK1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_BK1,
+        true,
+        BBlockLdsExtraN,
+        D0BlockTransferSrcScalarPerVector,
+        B1BlockTransferThreadClusterLengths_BK0_N_BK1,
+        B1BlockTransferThreadClusterArrangeOrder,
+        B1BlockTransferSrcAccessOrder,
+        B1BlockTransferSrcVectorDim,
+        B1BlockTransferSrcScalarPerVector,
+        B1BlockTransferDstScalarPerVector_BK1,
+        true,
+        B1BlockLdsExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        CShuffleBlockTransferScalarPerVector_NPerBlock,
+        LoopSched,
+        Transform::matrix_padder.PadN,
+        MaskingSpec != MaskingSpecialization::MaskDisabled,
+        Deterministic>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const InputDataType* p_a_grid,
+                 const InputDataType* p_b_grid,
+                 ZDataType* p_z_grid,
+                 const InputDataType* p_b1_grid,
+                 const InputDataType* p_c_grid, // for dS
+                 const LSEDataType* p_lse_grid,
+                 const InputDataType* p_ygrad_grid,
+                 OutputDataType* p_qgrad_grid,
+                 OutputDataType* p_kgrad_grid,
+                 OutputDataType* p_vgrad_grid,
+                 const D0DataType* p_acc0_bias,
+                 const D1DataType* p_acc1_bias,
+                 D0DataType* p_d0grad_grid,
+                 D1DataType* p_d1grad_grid,
+                 const std::vector<index_t>& a_gs_ms_ks_lengths,
+                 const std::vector<index_t>& a_gs_ms_ks_strides,
+                 const std::vector<index_t>& b_gs_ns_ks_lengths,
+                 const std::vector<index_t>& b_gs_ns_ks_strides,
+                 const std::vector<index_t>& z_gs_ms_ns_lengths,
+                 const std::vector<index_t>& z_gs_ms_ns_strides,
+                 const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+                 const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+                 const std::vector<index_t>& c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+                 const std::vector<index_t>& c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+                 const std::vector<index_t>& lse_gs_ms_lengths,
+                 const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
+                 const std::vector<index_t>& bgrad_gs_ns_ks_strides,
+                 const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
+                 const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
+                 const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
+                 const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
+                 const std::vector<ck::index_t>&
+                     acc1_bias_gs_ms_gemm1ns_lengths, // acc1_bias_gs_ms_os_lengths
+                 const std::vector<ck::index_t>&
+                     acc1_bias_gs_ms_gemm1ns_strides, // acc1_bias_gs_ms_os_strides
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 AccElementwiseOperation acc_element_op,
+                 B1ElementwiseOperation b1_element_op,
+                 CElementwiseOperation c_element_op,
+                 float p_drop,
+                 std::tuple<unsigned long long, unsigned long long> seeds)
+            : p_a_grid_{p_a_grid},
+              p_b_grid_{p_b_grid},
+              p_d0_grid_{p_acc0_bias},
+              p_z_grid_{p_z_grid},
+              p_b1_grid_{p_b1_grid},
+              p_c_grid_{p_c_grid},
+              p_lse_grid_{p_lse_grid},
+              p_ygrad_grid_{p_ygrad_grid},
+              p_qgrad_grid_{p_qgrad_grid},
+              p_kgrad_grid_{p_kgrad_grid},
+              p_vgrad_grid_{p_vgrad_grid},
+              p_d0grad_grid_{p_d0grad_grid},
+              a_grid_desc_ak0_m_ak1_{
+                  DeviceOp::MakeAGridDescriptor_AK0_M_AK1(a_gs_ms_ks_lengths, a_gs_ms_ks_strides)},
+              b_grid_desc_bk0_n_bk1_{
+                  DeviceOp::MakeBGridDescriptor_BK0_N_BK1(b_gs_ns_ks_lengths, b_gs_ns_ks_strides)},
+              bgrad_grid_desc_bk0_n_bk1_{DeviceOp::MakeBGridDescriptor_BK0_N_BK1(
+                  bgrad_gs_ns_ks_lengths, bgrad_gs_ns_ks_strides)},
+              z_grid_desc_m_n_{MakeZGridDescriptor_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
+              b1_grid_desc_bk0_n_bk1_{DeviceOp::MakeB1GridDescriptor_BK0_N_BK1(
+                  b1_gs_gemm1ns_gemm1ks_lengths, b1_gs_gemm1ns_gemm1ks_strides)},
+              b1grad_grid_desc_bk0_n_bk1_{DeviceOp::MakeB1GridDescriptor_BK0_N_BK1(
+                  b1grad_gs_gemm1ns_gemm1ks_lengths, b1grad_gs_gemm1ns_gemm1ks_strides)},
+              y_grid_desc_m_o_{Transform::MakeCGridDescriptor_M_N(c_gs_ms_gemm1ns_lengths,
+                                                                  c_gs_ms_gemm1ns_strides)},
+              lse_grid_desc_m_{DeviceOp::MakeLSEGridDescriptor_M(lse_gs_ms_lengths[NumDimG])},
+              k_grid_desc_n_k_{
+                  Transform::MakeB0GridDescriptor_N_K(b_gs_ns_ks_lengths, b_gs_ns_ks_strides)},
+              ygrad_grid_desc_m0_o_m1_{DeviceOp::MakeYGradGridDescriptor_M0_O_M1(y_grid_desc_m_o_)},
+              // batch offsets
+              a_grid_desc_g_m_k_{
+                  Transform::MakeAGridDescriptor_G_M_K(a_gs_ms_ks_lengths, a_gs_ms_ks_strides)},
+              b_grid_desc_g_n_k_{
+                  Transform::MakeB0GridDescriptor_G_N_K(b_gs_ns_ks_lengths, b_gs_ns_ks_strides)},
+              b1_grid_desc_g_n_k_{Transform::MakeB1GridDescriptor_G_N_K(
+                  b1_gs_gemm1ns_gemm1ks_lengths, b1_gs_gemm1ns_gemm1ks_strides)},
+              c_grid_desc_g_m_n_{Transform::MakeCGridDescriptor_G_M_N(c_gs_ms_gemm1ns_lengths,
+                                                                      c_gs_ms_gemm1ns_strides)},
+              z_grid_desc_g_m_n_{
+                  Transform::MakeC0GridDescriptor_G_M_N(z_gs_ms_ns_lengths, z_gs_ms_ns_strides)},
+              bgrad_grid_desc_g_n_k_{Transform::MakeB0GridDescriptor_G_N_K(bgrad_gs_ns_ks_lengths,
+                                                                           bgrad_gs_ns_ks_strides)},
+              b1grad_grid_desc_g_n_k_{Transform::MakeB1GridDescriptor_G_N_K(
+                  b1grad_gs_gemm1ns_gemm1ks_lengths, b1grad_gs_gemm1ns_gemm1ks_strides)},
+              y_grid_desc_mblock_mperblock_oblock_operblock_{},
+              block_2_ctile_map_{GridwiseGemm::MakeDefaultBlock2CTileMap(k_grid_desc_n_k_)},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              acc_element_op_{acc_element_op},
+              b1_element_op_{b1_element_op},
+              c_element_op_{c_element_op},
+              c0_matrix_mask_{a_grid_desc_g_m_k_.GetLength(I1), b_grid_desc_g_n_k_.GetLength(I1)},
+              raw_lengths_mz_nz_kz_gemm1nz_{a_gs_ms_ks_lengths[NumDimG + NumDimM - 1],
+                                            b_gs_ns_ks_lengths[NumDimG + NumDimN - 1],
+                                            b_gs_ns_ks_lengths[NumDimG + NumDimN + NumDimK - 1],
+                                            b1_gs_gemm1ns_gemm1ks_lengths[NumDimG + NumDimO - 1]},
+              a_mz_kz_strides_{a_gs_ms_ks_strides[NumDimG + NumDimM - 1],
+                               a_gs_ms_ks_strides[NumDimG + NumDimM + NumDimK - 1]},
+              b_nz_kz_strides_{b_gs_ns_ks_strides[NumDimG + NumDimN - 1],
+                               b_gs_ns_ks_strides[NumDimG + NumDimN + NumDimK - 1]},
+              b1_nz_kz_strides_{b1_gs_gemm1ns_gemm1ks_strides[NumDimG + NumDimO - 1],
+                                b1_gs_gemm1ns_gemm1ks_strides[NumDimG + NumDimO + NumDimN - 1]},
+              c_mz_gemm1nz_strides_{c_gs_ms_gemm1ns_strides[NumDimG + NumDimM - 1],
+                                    c_gs_ms_gemm1ns_strides[NumDimG + NumDimM + NumDimO - 1]},
+              batch_count_{c_grid_desc_g_m_n_.GetLength(I0)},
+              h_ratio_{c_grid_desc_g_m_n_.GetLength(I0) / b_grid_desc_g_n_k_.GetLength(I0)},
+              p_drop_{p_drop}
+        {
+            // TODO: implement bias addition
+            ignore = p_acc1_bias;
+            ignore = p_d1grad_grid;
+            ignore = acc1_bias_gs_ms_gemm1ns_lengths;
+            ignore = acc1_bias_gs_ms_gemm1ns_strides;
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
+                                           b_grid_desc_bk0_n_bk1_,
+                                           b1_grid_desc_bk0_n_bk1_,
+                                           y_grid_desc_m_o_))
+            {
+                y_grid_desc_mblock_mperblock_oblock_operblock_ =
+                    GridwiseGemm::MakeYGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock(
+                        y_grid_desc_m_o_);
+            }
+
+            if constexpr(!is_same<D0DataType, void>::value)
+            {
+                const auto d0_grid_desc_m_n = MakeD0GridDescriptor_M_N(acc0_bias_gs_ms_ns_lengths,
+                                                                       acc0_bias_gs_ms_ns_strides);
+                d0_grid_desc_m0_n0_m1_m2_n1_m3_ =
+                    GridwiseGemm::MakeD0GridDescriptor_M0_N0_M1_M2_N1_M3(d0_grid_desc_m_n);
+
+                d0_grid_desc_g_m_n_ = Transform::MakeC0GridDescriptor_G_M_N(
+                    acc0_bias_gs_ms_ns_lengths, acc0_bias_gs_ms_ns_strides);
+
+                d0_n_length_stride_.push_back(acc0_bias_gs_ms_ns_lengths[NumDimG + NumDimM]);
+                d0_n_length_stride_.push_back(acc0_bias_gs_ms_ns_strides[NumDimG + NumDimM]);
+            }
+
+            compute_base_ptr_of_batch_ = ComputeBasePtrOfStridedBatch(
+                a_grid_desc_g_m_k_,
+                b_grid_desc_g_n_k_,
+                d0_grid_desc_g_m_n_,
+                z_grid_desc_g_m_n_,
+                b1_grid_desc_g_n_k_,
+                c_grid_desc_g_m_n_,
+                bgrad_grid_desc_g_n_k_,
+                b1grad_grid_desc_g_n_k_,
+                type_convert<index_t>(lse_grid_desc_m_.GetElementSpaceSize()));
+
+            seed_   = std::get<0>(seeds);
+            offset_ = std::get<1>(seeds);
+
+            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_ =
+                GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3(z_grid_desc_m_n_);
+            // Print();
+
+            m_raw_padded_ = GridwiseGemm::GetPaddedSize(raw_lengths_mz_nz_kz_gemm1nz_[0]);
+            n_raw_padded_ = GridwiseGemm::GetPaddedSize(raw_lengths_mz_nz_kz_gemm1nz_[1]);
+        }
+
+        void Print() const
+        {
+            std::cout << "a_grid_desc_g_m_k_: " << a_grid_desc_g_m_k_.GetLength(I0) << ", "
+                      << a_grid_desc_g_m_k_.GetLength(I1) << ", "
+                      << a_grid_desc_g_m_k_.GetLength(I2) << '\n';
+            // a_grid_desc_g_m_k_.Print();
+            std::cout << "b_grid_desc_g_n_k_: " << b_grid_desc_g_n_k_.GetLength(I0) << ", "
+                      << b_grid_desc_g_n_k_.GetLength(I1) << ", "
+                      << b_grid_desc_g_n_k_.GetLength(I2) << '\n';
+            // b_grid_desc_g_n_k_.Print();
+            std::cout << "b1_grid_desc_g_n_k_: " << b1_grid_desc_g_n_k_.GetLength(I0) << ", "
+                      << b1_grid_desc_g_n_k_.GetLength(I1) << ", "
+                      << b1_grid_desc_g_n_k_.GetLength(I2) << '\n';
+            // b1_grid_desc_g_n_k_.Print();
+            std::cout << "c_grid_desc_g_m_o_: " << c_grid_desc_g_m_n_.GetLength(I0) << ", "
+                      << c_grid_desc_g_m_n_.GetLength(I1) << ", "
+                      << c_grid_desc_g_m_n_.GetLength(I2) << '\n';
+            // c_grid_desc_g_m_n_.Print();
+            std::cout << "k_grid_desc_n_k_: " << k_grid_desc_n_k_.GetLength(I0) << ", "
+                      << k_grid_desc_n_k_.GetLength(I1) << '\n';
+            std::cout << "ygrad_grid_desc_m0_o_m1_: " << ygrad_grid_desc_m0_o_m1_.GetLength(I0)
+                      << ", " << ygrad_grid_desc_m0_o_m1_.GetLength(I1) << ", "
+                      << ygrad_grid_desc_m0_o_m1_.GetLength(I2) << '\n';
+            std::cout << "d0_grid_desc_g_m_n_: " << d0_grid_desc_g_m_n_.GetLength(I0) << ", "
+                      << d0_grid_desc_g_m_n_.GetLength(I1) << ", "
+                      << d0_grid_desc_g_m_n_.GetLength(I2) << '\n';
+            std::cout << "bgrad_grid_desc_g_n_k_: " << bgrad_grid_desc_g_n_k_.GetLength(I0) << ", "
+                      << bgrad_grid_desc_g_n_k_.GetLength(I1) << ", "
+                      << bgrad_grid_desc_g_n_k_.GetLength(I2) << '\n';
+            // bgrad_grid_desc_g_n_k_.Print();
+            std::cout << "b1grad_grid_desc_g_n_k_: " << b1grad_grid_desc_g_n_k_.GetLength(I0)
+                      << ", " << b1grad_grid_desc_g_n_k_.GetLength(I1) << ", "
+                      << b1grad_grid_desc_g_n_k_.GetLength(I2) << '\n';
+            // b1grad_grid_desc_g_n_k_.Print();
+        }
+
+        // pointers
+        const InputDataType* p_a_grid_;
+        const InputDataType* p_b_grid_;
+        const D0DataType* p_d0_grid_;
+        ZDataType* p_z_grid_;
+        const InputDataType* p_b1_grid_;
+        const InputDataType* p_c_grid_;
+        const LSEDataType* p_lse_grid_;
+        const InputDataType* p_ygrad_grid_;
+        OutputDataType* p_qgrad_grid_;
+        OutputDataType* p_kgrad_grid_;
+        OutputDataType* p_vgrad_grid_;
+        D0DataType* p_d0grad_grid_;
+
+        // tensor descriptor
+        AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
+        BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
+        BGridDesc_BK0_N_BK1 bgrad_grid_desc_bk0_n_bk1_;
+        typename GridwiseGemm::D0GridDescriptor_M0_N0_M1_M2_N1_M3 d0_grid_desc_m0_n0_m1_m2_n1_m3_;
+        ZGridDesc_M_N z_grid_desc_m_n_;
+        B1GridDesc_BK0_N_BK1 b1_grid_desc_bk0_n_bk1_;
+        B1GridDesc_BK0_N_BK1 b1grad_grid_desc_bk0_n_bk1_;
+        YGridDesc_M_O y_grid_desc_m_o_;
+        LSEGridDesc_M lse_grid_desc_m_;
+        KGridDesc_N_K k_grid_desc_n_k_;
+        YGradGridDesc_M0_O_M1 ygrad_grid_desc_m0_o_m1_;
+
+        // batch offsets
+        AGridDesc_G_M_K a_grid_desc_g_m_k_;
+        BGridDesc_G_N_K b_grid_desc_g_n_k_;
+        D0GridDesc_G_M_N d0_grid_desc_g_m_n_;
+        B1GridDesc_G_N_K b1_grid_desc_g_n_k_;
+        CGridDesc_G_M_N c_grid_desc_g_m_n_;
+        ZGridDesc_G_M_N z_grid_desc_g_m_n_;
+        BGridDesc_G_N_K bgrad_grid_desc_g_n_k_;
+        B1GridDesc_G_N_K b1grad_grid_desc_g_n_k_;
+        typename GridwiseGemm::YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock
+            y_grid_desc_mblock_mperblock_oblock_operblock_;
+
+        typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3
+            c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_;
+
+        // block-to-c-tile map
+        typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
+
+        // element-wise op
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        AccElementwiseOperation acc_element_op_;
+        B1ElementwiseOperation b1_element_op_;
+        CElementwiseOperation c_element_op_;
+
+        // check C0 masking and padding
+        C0MatrixMask c0_matrix_mask_;
+
+        // For robust IsSupportedArgument() check
+        std::vector<index_t> raw_lengths_mz_nz_kz_gemm1nz_;
+        std::vector<index_t> a_mz_kz_strides_;
+        std::vector<index_t> b_nz_kz_strides_;
+        std::vector<index_t> b1_nz_kz_strides_;
+        std::vector<index_t> c_mz_gemm1nz_strides_;
+
+        index_t batch_count_;
+        index_t h_ratio_;
+        ComputeBasePtrOfStridedBatch compute_base_ptr_of_batch_;
+
+        float p_drop_;
+        unsigned long long seed_;
+        unsigned long long offset_;
+
+        index_t m_raw_padded_;
+        index_t n_raw_padded_;
+
+        // raw data
+        std::vector<ck::index_t> d0_n_length_stride_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            if(!DeviceOp::IsSupportedArgument(arg))
+            {
+                throw std::runtime_error("wrong! unsupported argument");
+            }
+
+            const index_t grid_size =
+                (Deterministic ? 1
+                               : arg.block_2_ctile_map_.CalculateGridSize(arg.k_grid_desc_n_k_)) *
+                arg.batch_count_;
+
+            // Gemm0_K
+            const auto K =
+                arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
+
+            float ave_time = 0;
+
+            auto launch_kernel = [&](auto has_main_k_block_loop_, auto is_dropout_) {
+                const auto kernel =
+                    kernel_batched_multihead_attention_backward_qloop_xdl_cshuffle_v3<
+                        GridwiseGemm,
+                        InputDataType,
+                        D0DataType,
+                        OutputDataType,
+                        ZDataType,
+                        LSEDataType,
+                        AElementwiseOperation,
+                        BElementwiseOperation,
+                        AccElementwiseOperation,
+                        B1ElementwiseOperation,
+                        CElementwiseOperation,
+                        DeviceOp::AGridDesc_AK0_M_AK1,
+                        DeviceOp::BGridDesc_BK0_N_BK1,
+                        typename GridwiseGemm::D0GridDescriptor_M0_N0_M1_M2_N1_M3,
+                        typename GridwiseGemm::ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3,
+                        DeviceOp::B1GridDesc_BK0_N_BK1,
+                        typename GridwiseGemm::YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock,
+                        DeviceOp::LSEGridDesc_M,
+                        DeviceOp::YGradGridDesc_M0_O_M1,
+                        typename GridwiseGemm::DefaultBlock2CTileMap,
+                        ComputeBasePtrOfStridedBatch,
+                        C0MatrixMask,
+                        has_main_k_block_loop_,
+                        is_dropout_,
+                        Deterministic>;
+
+                return launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    arg.p_a_grid_,
+                    arg.p_b_grid_,
+                    arg.p_d0_grid_,
+                    arg.p_z_grid_,
+                    arg.p_b1_grid_,
+                    arg.p_c_grid_,
+                    arg.p_lse_grid_,
+                    arg.p_ygrad_grid_,
+                    arg.p_qgrad_grid_,
+                    arg.p_kgrad_grid_,
+                    arg.p_d0grad_grid_,
+                    arg.p_vgrad_grid_,
+                    arg.a_element_op_,
+                    arg.b_element_op_,
+                    arg.acc_element_op_,
+                    arg.b1_element_op_,
+                    arg.c_element_op_,
+                    arg.a_grid_desc_ak0_m_ak1_,
+                    arg.b_grid_desc_bk0_n_bk1_,
+                    arg.bgrad_grid_desc_bk0_n_bk1_,
+                    arg.d0_grid_desc_m0_n0_m1_m2_n1_m3_,
+                    arg.c_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3_,
+                    arg.b1_grid_desc_bk0_n_bk1_,
+                    arg.b1grad_grid_desc_bk0_n_bk1_,
+                    arg.y_grid_desc_mblock_mperblock_oblock_operblock_,
+                    arg.lse_grid_desc_m_,
+                    arg.ygrad_grid_desc_m0_o_m1_,
+                    arg.block_2_ctile_map_,
+                    arg.batch_count_,
+                    arg.h_ratio_,
+                    arg.block_2_ctile_map_.CalculateGridSize(arg.k_grid_desc_n_k_),
+                    arg.compute_base_ptr_of_batch_,
+                    arg.c0_matrix_mask_,
+                    arg.p_drop_,
+                    arg.seed_,
+                    arg.offset_,
+                    arg.m_raw_padded_,
+                    arg.n_raw_padded_);
+            };
+
+            // Gemm1_K is split into Gemm1_K0/K1 where K1 is known at compile time, so we only need
+            // to concern Gemm0's loop
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                if(arg.p_drop_ > 0.0)
+                    ave_time = launch_kernel(integral_constant<bool, true>{},
+                                             integral_constant<bool, true>{});
+                else
+                    ave_time = launch_kernel(integral_constant<bool, true>{},
+                                             integral_constant<bool, false>{});
+            }
+            else
+            {
+                if(arg.p_drop_ > 0.0)
+                    ave_time = launch_kernel(integral_constant<bool, false>{},
+                                             integral_constant<bool, true>{});
+                else
+                    ave_time = launch_kernel(integral_constant<bool, false>{},
+                                             integral_constant<bool, false>{});
+            }
+            return ave_time;
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+#if DEBUG_LOG
+        arg.Print();
+#endif
+
+        if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a" ||
+             ck::get_device_name() == "gfx940" || ck::get_device_name() == "gfx941" ||
+             ck::get_device_name() == "gfx942"))
+        {
+            return false;
+        }
+
+        // TODO: Check if tensor specialization & strides mismatch
+
+        // Check if C permute dimension matches GEMM + GEMM shape
+        const index_t c_g       = arg.c_grid_desc_g_m_n_.GetLength(I0); // unpadded
+        const index_t b_g       = arg.b_grid_desc_g_n_k_.GetLength(I0);
+        const index_t c_m       = arg.y_grid_desc_m_o_.GetLength(I0);
+        const index_t c_gemm1n  = arg.y_grid_desc_m_o_.GetLength(I1);
+        const index_t a_m       = arg.a_grid_desc_ak0_m_ak1_.GetLength(I1);
+        const index_t b1_gemm1n = arg.b1_grid_desc_bk0_n_bk1_.GetLength(I1);
+
+        if(!(c_g == arg.batch_count_ && c_m == a_m && c_gemm1n == b1_gemm1n && c_g % b_g == 0))
+        {
+            return false;
+        }
+
+        if constexpr(!is_same<D0DataType, void>::value)
+        {
+            if(arg.d0_n_length_stride_[1] == 1 &&
+               arg.d0_n_length_stride_[0] % D0BlockTransferSrcScalarPerVector != 0)
+            {
+                return false;
+            }
+            if(arg.d0_n_length_stride_[1] != 1 && D0BlockTransferSrcScalarPerVector != 1)
+            {
+                return false;
+            }
+        }
+        // Note: we need raw lengths since threadwise copy can not handle vector load when part of
+        // vector is out of bounds
+        // Note: need lowest dim in Ms/Ns/Ks/Os, not merged M/N/K/O
+        const auto MzRaw      = arg.raw_lengths_mz_nz_kz_gemm1nz_[0];
+        const auto NzRaw      = arg.raw_lengths_mz_nz_kz_gemm1nz_[1];
+        const auto KzRaw      = arg.raw_lengths_mz_nz_kz_gemm1nz_[2];
+        const auto Gemm1NzRaw = arg.raw_lengths_mz_nz_kz_gemm1nz_[3];
+
+        // Check scalar per vector requirement
+        const auto a_extent_lowest  = ABlockTransferSrcVectorDim == 2 ? KzRaw : MzRaw;
+        const auto b_extent_lowest  = BBlockTransferSrcVectorDim == 2 ? KzRaw : NzRaw;
+        const auto b1_extent_lowest = B1BlockTransferSrcVectorDim == 2 ? NzRaw : Gemm1NzRaw;
+        const auto c_extent_lowest  = Gemm1NzRaw;
+
+        if(!(a_extent_lowest % ABlockTransferSrcScalarPerVector == 0 &&
+             b_extent_lowest % BBlockTransferSrcScalarPerVector == 0 &&
+             b1_extent_lowest % B1BlockTransferSrcScalarPerVector == 0 &&
+             c_extent_lowest % CShuffleBlockTransferScalarPerVector_NPerBlock == 0))
+        {
+            return false;
+        }
+
+        // saving dQ data with atomic_add instruction, so KzRaw must be a multiple of 2
+        if constexpr(is_same<OutputDataType, half_t>::value ||
+                     is_same<OutputDataType, bhalf_t>::value)
+        {
+            if(KzRaw % 2 != 0)
+            {
+                std::cout << "K_q must be a multiple of 2" << std::endl;
+                return false;
+            }
+        }
+
+        // Check vector load/store requirement
+        const auto a_stride_lowest =
+            ABlockTransferSrcVectorDim == 2 ? arg.a_mz_kz_strides_[1] : arg.a_mz_kz_strides_[0];
+        const auto b_stride_lowest =
+            BBlockTransferSrcVectorDim == 2 ? arg.b_nz_kz_strides_[1] : arg.b_nz_kz_strides_[0];
+        const auto b1_stride_lowest =
+            B1BlockTransferSrcVectorDim == 2 ? arg.b1_nz_kz_strides_[1] : arg.b1_nz_kz_strides_[0];
+        const auto c_stride_lowest =
+            arg.c_mz_gemm1nz_strides_[1]; // cshuffle assumes lowest dim in Gemm1Ns to be contiguous
+
+        if(!(a_stride_lowest == 1 || b_stride_lowest == 1 || b1_stride_lowest == 1 ||
+             c_stride_lowest == 1))
+        {
+            return false;
+        }
+
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.b1_grid_desc_bk0_n_bk1_,
+                                           arg.y_grid_desc_m_o_);
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto
+    MakeArgument(const InputDataType* p_a,
+                 const InputDataType* p_b,
+                 ZDataType* p_z,
+                 const InputDataType* p_b1,
+                 const InputDataType* p_c,
+                 const LSEDataType* p_lse,
+                 const InputDataType* p_ygrad_grid,
+                 OutputDataType* p_qgrad_grid,
+                 OutputDataType* p_kgrad_grid,
+                 OutputDataType* p_vgrad_grid,
+                 const D0DataType* p_acc0_bias,
+                 const D1DataType* p_acc1_bias,
+                 D0DataType* p_d0grad_grid,
+                 D1DataType* p_d1grad_grid,
+                 const std::vector<index_t>& a_gs_ms_ks_lengths,
+                 const std::vector<index_t>& a_gs_ms_ks_strides,
+                 const std::vector<index_t>& b_gs_ns_ks_lengths,
+                 const std::vector<index_t>& b_gs_ns_ks_strides,
+                 const std::vector<index_t>& z_gs_ms_ns_lengths,
+                 const std::vector<index_t>& z_gs_ms_ns_strides,
+                 const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+                 const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+                 const std::vector<index_t>& c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+                 const std::vector<index_t>& c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+                 const std::vector<index_t>& lse_gs_ms_lengths,
+                 const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
+                 const std::vector<index_t>& bgrad_gs_ns_ks_strides,
+                 const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
+                 const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
+                 const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
+                 const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
+                 const std::vector<ck::index_t>&
+                     acc1_bias_gs_ms_gemm1ns_lengths, // acc1_bias_gs_ms_os_lengths
+                 const std::vector<ck::index_t>&
+                     acc1_bias_gs_ms_gemm1ns_strides, // acc1_bias_gs_ms_os_strides
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 AccElementwiseOperation acc_element_op,
+                 B1ElementwiseOperation b1_element_op,
+                 CElementwiseOperation c_element_op,
+                 float p_drop,
+                 std::tuple<unsigned long long, unsigned long long> seeds)
+    {
+        return Argument{p_a,
+                        p_b,
+                        p_z,
+                        p_b1,
+                        p_c,
+                        p_lse,
+                        p_ygrad_grid,
+                        p_qgrad_grid,
+                        p_kgrad_grid,
+                        p_vgrad_grid,
+                        p_acc0_bias,
+                        p_acc1_bias,
+                        p_d0grad_grid,
+                        p_d1grad_grid,
+                        a_gs_ms_ks_lengths,
+                        a_gs_ms_ks_strides,
+                        b_gs_ns_ks_lengths,
+                        b_gs_ns_ks_strides,
+                        z_gs_ms_ns_lengths,
+                        z_gs_ms_ns_strides,
+                        b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+                        b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+                        c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+                        c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+                        lse_gs_ms_lengths,
+                        bgrad_gs_ns_ks_lengths,
+                        bgrad_gs_ns_ks_strides,
+                        b1grad_gs_gemm1ns_gemm1ks_lengths,
+                        b1grad_gs_gemm1ns_gemm1ks_strides,
+                        acc0_bias_gs_ms_ns_lengths,
+                        acc0_bias_gs_ms_ns_strides,
+                        acc1_bias_gs_ms_gemm1ns_lengths, // acc1_bias_gs_ms_os_lengths
+                        acc1_bias_gs_ms_gemm1ns_strides, // acc1_bias_gs_ms_os_strides
+                        a_element_op,
+                        b_element_op,
+                        acc_element_op,
+                        b1_element_op,
+                        c_element_op,
+                        p_drop,
+                        seeds};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    // FIXME: constness
+    std::unique_ptr<BaseArgument> MakeArgumentPointer(
+        const void* p_a,
+        const void* p_b,
+        void* p_z,
+        const void* p_b1,
+        const void* p_c,
+        const void* p_lse,
+        const void* p_ygrad_grid,
+        void* p_qgrad_grid,
+        void* p_kgrad_grid,
+        void* p_vgrad_grid,
+        const void* p_acc0_bias,
+        const void* p_acc1_bias,
+        void* p_d0grad_grid,
+        void* p_d1grad_grid,
+        const std::vector<index_t>& a_gs_ms_ks_lengths,
+        const std::vector<index_t>& a_gs_ms_ks_strides,
+        const std::vector<index_t>& b_gs_ns_ks_lengths,
+        const std::vector<index_t>& b_gs_ns_ks_strides,
+        const std::vector<index_t>& z_gs_ms_ns_lengths,
+        const std::vector<index_t>& z_gs_ms_ns_strides,
+        const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+        const std::vector<index_t>& b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+        const std::vector<index_t>& c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+        const std::vector<index_t>& c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+        const std::vector<index_t>& lse_gs_ms_lengths,
+        const std::vector<index_t>& bgrad_gs_ns_ks_lengths,
+        const std::vector<index_t>& bgrad_gs_ns_ks_strides,
+        const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_lengths,
+        const std::vector<index_t>& b1grad_gs_gemm1ns_gemm1ks_strides,
+        const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_lengths,
+        const std::vector<ck::index_t>& acc0_bias_gs_ms_ns_strides,
+        const std::vector<ck::index_t>&
+            acc1_bias_gs_ms_gemm1ns_lengths, // acc1_bias_gs_ms_os_lengths
+        const std::vector<ck::index_t>&
+            acc1_bias_gs_ms_gemm1ns_strides, // acc1_bias_gs_ms_os_strides
+        AElementwiseOperation a_element_op,
+        BElementwiseOperation b_element_op,
+        AccElementwiseOperation acc_element_op,
+        B1ElementwiseOperation b1_element_op,
+        CElementwiseOperation c_element_op,
+        float p_drop,
+        std::tuple<unsigned long long, unsigned long long> seeds) // override
+    {
+        return std::make_unique<Argument>(
+            static_cast<const InputDataType*>(p_a),
+            static_cast<const InputDataType*>(p_b),
+            static_cast<ZDataType*>(p_z),
+            static_cast<const InputDataType*>(p_b1),
+            static_cast<const InputDataType*>(p_c),
+            static_cast<const LSEDataType*>(p_lse),
+            static_cast<const InputDataType*>(p_ygrad_grid),
+            static_cast<OutputDataType*>(p_qgrad_grid),
+            static_cast<OutputDataType*>(p_kgrad_grid),
+            static_cast<OutputDataType*>(p_vgrad_grid),
+            static_cast<const D0DataType*>(p_acc0_bias), // cast in struct Argument
+            static_cast<const D1DataType*>(p_acc1_bias), // cast in struct Argument
+            static_cast<D0DataType*>(p_d0grad_grid),
+            static_cast<D1DataType*>(p_d1grad_grid),
+            a_gs_ms_ks_lengths,
+            a_gs_ms_ks_strides,
+            b_gs_ns_ks_lengths,
+            b_gs_ns_ks_strides,
+            z_gs_ms_ns_lengths,
+            z_gs_ms_ns_strides,
+            b1_gs_gemm1ns_gemm1ks_lengths, // b1_gs_os_ns_lengths
+            b1_gs_gemm1ns_gemm1ks_strides, // b1_gs_os_ns_strides
+            c_gs_ms_gemm1ns_lengths,       // c_gs_ms_os_lengths
+            c_gs_ms_gemm1ns_strides,       // c_gs_ms_os_strides
+            lse_gs_ms_lengths,
+            bgrad_gs_ns_ks_lengths,
+            bgrad_gs_ns_ks_strides,
+            b1grad_gs_gemm1ns_gemm1ks_lengths,
+            b1grad_gs_gemm1ns_gemm1ks_strides,
+            acc0_bias_gs_ms_ns_lengths,
+            acc0_bias_gs_ms_ns_strides,
+            acc1_bias_gs_ms_gemm1ns_lengths,
+            acc1_bias_gs_ms_gemm1ns_strides,
+            a_element_op,
+            b_element_op,
+            acc_element_op,
+            b1_element_op,
+            c_element_op,
+            p_drop,
+            seeds);
+    }
+
+    // polymorphic
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() // override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    // polymorphic
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V3"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << KPerBlock << ", "
+            << AK1 << ", "
+            << BK1 << ", "
+            << MPerBlock << ", "
+            << Gemm1NPerBlock << ", "
+            << Gemm1KPerBlock << ", "
+            << Gemm2KPerBlock << ", "
+            << B1K1 << ", "
+            << getGemmSpecializationString(GemmSpec) << ", "
+            << "ASpec" << getTensorSpecializationString(ASpec) << ", "
+            << "B0Spec" << getTensorSpecializationString(BSpec) << ", "
+            << "B1Spec" << getTensorSpecializationString(B1Spec) << ", "
+            << "CSpec" << getTensorSpecializationString(CSpec) << ", "
+            << getMaskingSpecializationString(MaskingSpec) << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

include/ck/tensor_operation/gpu/grid/gridwise_batched_mha_bwd_xdl_cshuffle_qloop_b2t_v3.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/utility/philox_rand.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_softmax.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_dropout.hpp"
+
+namespace ck {
+
+template <typename InputDataType,
+          typename D0DataType,
+          typename OutputDataType,
+          typename ZDataType,
+          typename GemmDataType,
+          typename FloatGemmAcc,
+          typename FloatCShuffle,
+          typename FloatLSE,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename SElementwiseOperation,
+          typename B1ElementwiseOperation,
+          typename CElementwiseOperation,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+          typename QGridDesc_K0_M_K1,
+          typename KGridDesc_K0_N_K1,
+          typename KGridDesc_N_K,
+          typename D0GridDesc_M_N,
+          typename ZGridDesc_M_N,
+          typename VGridDesc_N0_O_N1,
+          typename YGridDesc_M_O,
+          typename LSEGridDesc_M,
+          index_t NumGemmKPrefetchStage,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t Gemm1NPerBlock,
+          index_t Gemm1KPerBlock,
+          index_t Gemm2KPerBlock,
+          index_t AK1Value,
+          index_t BK1Value,
+          index_t B1K1Value,
+          index_t MPerXdl,
+          index_t NPerXdl,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          index_t Gemm1NXdlPerWave,
+          index_t Gemm2NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          bool AThreadTransferSrcResetCoordinateAfterRun, // ignored
+          index_t ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          bool BThreadTransferSrcResetCoordinateAfterRun, // ignored
+          index_t BBlockLdsExtraN,
+          index_t D0BlockTransferSrcScalarPerVector,
+          typename B1BlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename B1BlockTransferThreadClusterArrangeOrder,
+          typename B1BlockTransferSrcAccessOrder,
+          index_t B1BlockTransferSrcVectorDim,
+          index_t B1BlockTransferSrcScalarPerVector,
+          index_t B1BlockTransferDstScalarPerVector_BK1,
+          bool B1ThreadTransferSrcResetCoordinateAfterRun,
+          index_t B1BlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+          LoopScheduler LoopSched,
+          bool PadN,
+          bool MaskOutUpperTriangle,
+          bool Deterministic,
+          PipelineVersion PipelineVer = PipelineVersion::v1>
+struct GridwiseBatchedMultiheadAttentionBackward_Qloop_Xdl_CShuffle_V3
+{
+    static_assert(AK1Value % ABlockTransferDstScalarPerVector_AK1 == 0);
+    static_assert(BK1Value % BBlockTransferDstScalarPerVector_BK1 == 0);
+    static_assert(B1K1Value % B1BlockTransferDstScalarPerVector_BK1 == 0);
+
+    static_assert(Gemm1NPerBlock % KPerBlock == 0);
+    static_assert(MPerBlock % Gemm1KPerBlock == 0);
+    static_assert(NPerBlock % Gemm2KPerBlock == 0);
+
+    static_assert(LoopSched == LoopScheduler::Default,
+                  "Non-default loop scheduler is currently not supported");
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+    static constexpr auto I4 = Number<4>{};
+    static constexpr auto I5 = Number<5>{};
+    static constexpr auto I6 = Number<6>{};
+    static constexpr auto I7 = Number<7>{};
+
+    static constexpr auto WaveSize = 64;
+    // K1 should be Number<...>
+    // Gemm0
+    static constexpr auto AK0 = Number<KPerBlock / AK1Value>{};
+    static constexpr auto BK0 = Number<KPerBlock / BK1Value>{};
+    static constexpr auto AK1 = Number<AK1Value>{};
+    static constexpr auto BK1 = Number<BK1Value>{};
+
+    static constexpr auto Gemm0MWaves = MPerBlock / (MPerXdl * MXdlPerWave);
+    static constexpr auto Gemm0NWaves = NPerBlock / (NPerXdl * NXdlPerWave);
+
+    // Gemm1
+    static constexpr auto B1K0 = Number<Gemm1KPerBlock / B1K1Value>{};
+    static constexpr auto B1K1 = Number<B1K1Value>{};
+
+    static constexpr auto mfma = MfmaSelector<GemmDataType, MPerXdl, NPerXdl>::selected_mfma;
+    static constexpr auto DropoutNThread = mfma.num_input_blks; // 2
+    // get_random_16x8() generates 16 random numbers each time
+    static constexpr auto DropoutTile = Number<DropoutNThread * 16>{}; // 32
+
+    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
+
+    using GridwiseGemmPipe = remove_cvref_t<decltype(
+        GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage>())>;
+
+    // C desc for source in blockwise copy
+    __host__ __device__ static constexpr auto
+    MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3(const ZGridDesc_M_N& z_grid_desc_m_n)
+    {
+        const auto M = z_grid_desc_m_n.GetLength(I0);
+        const auto N = z_grid_desc_m_n.GetLength(I1);
+
+        constexpr auto M3 = mfma.num_groups_per_blk;
+        constexpr auto M4 = mfma.num_input_blks;
+        constexpr auto M5 = mfma.group_size;
+
+        return transform_tensor_descriptor(
+            z_grid_desc_m_n,
+            make_tuple(make_unmerge_transform(
+                           make_tuple(M / MPerBlock, MXdlPerWave, Gemm0MWaves, M3, M4, M5)),
+                       make_unmerge_transform(
+                           make_tuple(N / NPerBlock, NXdlPerWave, Gemm0NWaves, NPerXdl))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 2, 4, 6, 7, 8>{}, Sequence<1, 3, 5, 9>{}));
+    }
+
+    __host__ __device__ static constexpr auto GetPaddedSize(const index_t size)
+    {
+        return math::integer_divide_ceil(size, DropoutTile) * DropoutTile;
+    }
+
+    __device__ static auto GetGemm0WaveIdx()
+    {
+        const index_t thread_id = get_thread_local_1d_id();
+
+        constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_merge_transform(make_tuple(Gemm0MWaves, Gemm0NWaves, WaveSize))),
+            make_tuple(Sequence<0, 1, 2>{}),
+            make_tuple(Sequence<0>{}));
+
+        return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
+    }
+
+    __device__ static auto GetGemm0WaveMNIdx(const index_t thread_id)
+    {
+        constexpr auto wave_threadid_to_mn_idx_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_merge_transform(make_tuple(WaveSize / MPerXdl, MPerXdl))),
+            make_tuple(Sequence<0, 1>{}),
+            make_tuple(Sequence<0>{}));
+
+        return wave_threadid_to_mn_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
+    }
+
+    __host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
+    {
+        // A matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(AK0, Number<MPerBlock>{}, AK1),
+            make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
+    }
+
+    __host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
+    {
+        // B matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(BK0, Number<NPerBlock>{}, BK1),
+            make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
+    }
+
+    template <typename AccThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2>
+    __host__ __device__ static constexpr auto GetA1SrcThreadDescriptor_AK0PerBlock_MPerBlock_AK1(
+        const AccThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2& acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2)
+    {
+        // acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 to a_src_thread_desc_k0_m_k1
+        // m0_m1_m2_m3 -> k0
+        // n0_n1_n2 -> m
+        // m4 -> k1
+        // NOTE: had to use merge_v3 or will spit out compilation errors
+        const auto m0 = acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I0);
+        const auto n0 = acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I1);
+        const auto m1 = acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I2);
+        const auto n1 = acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I3);
+        const auto m2 = acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I4);
+        const auto m3 = acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I5);
+        const auto m4 = acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I6);
+        const auto n2 = acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I7);
+
+        return transform_tensor_descriptor(
+            acc_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+            make_tuple(make_merge_transform_v3_division_mod(make_tuple(m0, m1, m2, m3)),
+                       make_merge_transform_v3_division_mod(make_tuple(n0, n1, n2)),
+                       make_pass_through_transform(m4)),
+            make_tuple(Sequence<0, 2, 4, 5>{}, Sequence<1, 3, 7>{}, Sequence<6>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+    }
+
+    __host__ __device__ static constexpr auto GetB1BlockDescriptor_BK0PerBlock_NPerBlock_BK1()
+    {
+        // B1 matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(B1K0, Number<Gemm1NPerBlock>{}, B1K1),
+            make_tuple(Number<Gemm1NPerBlock + B1BlockLdsExtraN>{} * B1K1, B1K1, I1));
+    }
+
+    __host__ __device__ static constexpr auto
+    GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
+    {
+        constexpr index_t MWave = Gemm0NWaves;
+        constexpr index_t NWave = Gemm0MWaves;
+
+        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            make_naive_tensor_descriptor_packed(
+                make_tuple(I1,
+                           Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
+                           I1,
+                           Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
+
+        return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    template <typename Gemm2Param>
+    __host__ __device__ static constexpr auto GetA2BlockDescriptor_K0_M_K1()
+    {
+        return make_naive_tensor_descriptor(
+            make_tuple(Number<Gemm2Param::A_K0>{},
+                       Number<Gemm2Param::Gemm2_M>{},
+                       Number<Gemm2Param::A_K1>{}),
+            make_tuple(Number<Gemm2Param::Gemm2_M + Gemm2Param::A_LdsPad>{} *
+                           Number<Gemm2Param::A_K1>{},
+                       Number<Gemm2Param::A_K1>{},
+                       I1));
+    }
+
+    template <typename Gemm2Param>
+    __host__ __device__ static constexpr auto GetB2BlockDescriptor_K0_N_K1()
+    {
+        return make_naive_tensor_descriptor(
+            make_tuple(Number<Gemm2Param::B_K0>{},
+                       Number<Gemm2Param::Gemm2_N>{},
+                       Number<Gemm2Param::B_K1>{}),
+            make_tuple(Number<Gemm2Param::Gemm2_N + Gemm2Param::B_LdsPad>{} *
+                           Number<Gemm2Param::B_K1>{},
+                       Number<Gemm2Param::B_K1>{},
+                       I1));
+    }
+
+    __host__ __device__ static constexpr bool
+    CheckValidity(const QGridDesc_K0_M_K1& q_grid_desc_k0_m_k1,
+                  const KGridDesc_K0_N_K1& k_grid_desc_k0_n_k1,
+                  const VGridDesc_N0_O_N1& v_grid_desc_n0_o_n1,
+                  const YGridDesc_M_O& y_grid_desc_m_o)
+    {
+        static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
+                          (NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
+                      "Invalid tuning param!");
+
+        const auto M = q_grid_desc_k0_m_k1.GetLength(I1);
+        const auto N = k_grid_desc_k0_n_k1.GetLength(I1);
+        const auto K = q_grid_desc_k0_m_k1.GetLength(I0) * q_grid_desc_k0_m_k1.GetLength(I2);
+        const auto O = v_grid_desc_n0_o_n1.GetLength(I1);
+
+        // This assumption reduces implemention complexity by categorizing 6 separate GEMMs into 3
+        // types of GEMM operations, therefore some code body can be reused accordingly
+        // P_MNK / dP_MNO Gemm (Gemm0 rcr)
+        // Y_MON / dQ_MKN Gemm (Gemm1 rrr)
+        // dV_NOM / dK_NKM Gemm (Gemm2 crr)
+        if(O != K)
+        {
+            std::cerr << "O = " << O << " K = " << K << std::endl;
+            std::cerr << "SizeK must be equal to SizeO (equal attention head size)" << '\n';
+            return false;
+        }
+
+        if(!(M == y_grid_desc_m_o.GetLength(I0) && O == y_grid_desc_m_o.GetLength(I1)))
+        {
+            std::cerr << "M = " << M << " O = " << O
+                      << " y_grid_desc_m_o = " << y_grid_desc_m_o.GetLength(I0) << " , "
+                      << y_grid_desc_m_o.GetLength(I1) << std::endl;
+            std::cerr << "Un-matched sizes!" << std::endl;
+            return false;
+        }
+
+        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0 &&
+             O % Gemm1NPerBlock == 0))
+        {
+            std::cerr << "M = " << M << " N = " << N << " O = " << O << std::endl;
+            std::cerr << "MPerBlock = " << MPerBlock << " NPerBlock = " << NPerBlock
+                      << " KPerBlock = " << KPerBlock << std::endl;
+            std::cerr << "Un-aligned sizes!" << std::endl;
+            return false;
+        }
+
+        // check gemm0 gridwise gemm pipeline
+        const auto num_gemm0_k_loop = K / KPerBlock;
+        if(!GridwiseGemmPipe::IsSupported(num_gemm0_k_loop))
+        {
+            return false;
+        }
+
+        // check gemm1 gridwise gemm pipeline
+        if(!(NPerBlock % Gemm1KPerBlock == 0))
+        {
+            return false;
+        }
+
+        const auto num_gemm1_k_inner_loop = NPerBlock / Gemm1KPerBlock;
+        if(!GridwiseGemmPipe::IsSupported(num_gemm1_k_inner_loop))
+        {
+            return false;
+        }
+
+        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
+        return true;
+    }
+
+    __host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
+    {
+        const index_t num_loop = K / KPerBlock;
+
+        return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
+    }
+
+    __host__ __device__ static constexpr auto
+    MakeYGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock(const YGridDesc_M_O& y_grid_desc_m_o)
+    {
+        const auto M = y_grid_desc_m_o.GetLength(I0);
+        const auto O = y_grid_desc_m_o.GetLength(I1);
+
+        const auto MBlock = M / MPerBlock;
+        const auto OBlock = O / Gemm1NPerBlock;
+
+        const auto y_grid_desc_mblock_mperblock_oblock_operblock = transform_tensor_descriptor(
+            y_grid_desc_m_o,
+            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
+                       make_unmerge_transform(make_tuple(OBlock, Number<Gemm1NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
+
+        return y_grid_desc_mblock_mperblock_oblock_operblock;
+    }
+
+    template <typename SrcBlockwiseGemm>
+    __host__ __device__ static constexpr auto
+    MakeLSEGridDescriptor_MB_M0_M1_M2_M3_M4(const LSEGridDesc_M& lse_grid_desc_m)
+    {
+        const index_t M      = lse_grid_desc_m.GetLength(I0);
+        const index_t MBlock = M / MPerBlock;
+        constexpr auto SrcBlockDesc_M0_N0_M1_N1_M2_M3_M4_N2 =
+            SrcBlockwiseGemm::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+        // M0 MXdlPerWave, M1 MWave, M2 num_groups_per_blk, M3 num_input_blks, M4 group_size
+        const auto M0 = SrcBlockDesc_M0_N0_M1_N1_M2_M3_M4_N2.GetLength(I0);
+        const auto M1 = SrcBlockDesc_M0_N0_M1_N1_M2_M3_M4_N2.GetLength(I2);
+        const auto M2 = SrcBlockDesc_M0_N0_M1_N1_M2_M3_M4_N2.GetLength(I4);
+        const auto M3 = SrcBlockDesc_M0_N0_M1_N1_M2_M3_M4_N2.GetLength(I5);
+        const auto M4 = SrcBlockDesc_M0_N0_M1_N1_M2_M3_M4_N2.GetLength(I6);
+
+        const auto lse_grid_desc_mb_m0_m1_m2_m3_m4 = transform_tensor_descriptor(
+            lse_grid_desc_m,
+            make_tuple(make_unmerge_transform(make_tuple(MBlock, M0, M1, M2, M3, M4))),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0, 1, 2, 3, 4, 5>{}));
+
+        return lse_grid_desc_mb_m0_m1_m2_m3_m4;
+    }
+
+    __device__ static auto MakeKGradGridDesc_NBlock_NPerBlock_OBlock_OPerBlock(
+        const KGridDesc_K0_N_K1& k_grid_desc_k0_n_k1)
+    {
+        const auto O0 = k_grid_desc_k0_n_k1.GetLength(I0);
+        const auto N  = k_grid_desc_k0_n_k1.GetLength(I1);
+        const auto O1 = k_grid_desc_k0_n_k1.GetLength(I2);
+        const auto O  = O0 * O1;
+
+        const auto NBlock = N / NPerBlock;
+        const auto OBlock = O / Gemm1NPerBlock;
+
+        const auto k_grid_desc_n_o = transform_tensor_descriptor(
+            k_grid_desc_k0_n_k1,
+            make_tuple(make_pass_through_transform(N),
+                       make_merge_transform_v3_division_mod(make_tuple(O0, O1))),
+            make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        return transform_tensor_descriptor(
+            k_grid_desc_n_o,
+            make_tuple(make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{})),
+                       make_unmerge_transform(make_tuple(OBlock, Number<Gemm1NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
+    }
+
+    __device__ static auto MakeVGradGridDesc_NBlock_NPerBlock_OBlock_OPerBlock(
+        const VGridDesc_N0_O_N1& v_grid_desc_n0_o_n1)
+    {
+        const auto N0 = v_grid_desc_n0_o_n1.GetLength(I0);
+        const auto O  = v_grid_desc_n0_o_n1.GetLength(I1);
+        const auto N1 = v_grid_desc_n0_o_n1.GetLength(I2);
+        const auto N  = N0 * N1;
+
+        const auto NBlock = N / NPerBlock;
+        const auto OBlock = O / Gemm1NPerBlock;
+
+        const auto v_grid_desc_n_o = transform_tensor_descriptor(
+            v_grid_desc_n0_o_n1,
+            make_tuple(make_pass_through_transform(O),
+                       make_merge_transform_v3_division_mod(make_tuple(N0, N1))),
+            make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
+            make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+        return transform_tensor_descriptor(
+            v_grid_desc_n_o,
+            make_tuple(make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{})),
+                       make_unmerge_transform(make_tuple(OBlock, Number<Gemm1NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
+    }
+
+    __device__ static auto MakeQGradGridDesc_M_K(const QGridDesc_K0_M_K1& q_grid_desc_k0_m_k1)
+    {
+        const auto K_K0 = q_grid_desc_k0_m_k1.GetLength(I0);
+        const auto M    = q_grid_desc_k0_m_k1.GetLength(I1);
+        const auto K_K1 = q_grid_desc_k0_m_k1.GetLength(I2);
+
+        return transform_tensor_descriptor(
+            q_grid_desc_k0_m_k1,
+            make_tuple(make_pass_through_transform(M),
+                       make_merge_transform_v3_division_mod(make_tuple(K_K0, K_K1))),
+            make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+    }
+
+    // return block_id to C matrix tile idx (m0, n0) mapping
+    __host__ __device__ static constexpr auto
+    MakeDefaultBlock2CTileMap(const KGridDesc_N_K& k_grid_desc_n_k)
+    {
+        return BlockToCTileMap_M00_N0_M01Adapt<NPerBlock, Gemm1NPerBlock, KGridDesc_N_K>(
+            k_grid_desc_n_k);
+    }
+
+    using YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock = remove_cvref_t<decltype(
+        MakeYGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock(YGridDesc_M_O{}))>;
+
+    using DefaultBlock2CTileMap =
+        remove_cvref_t<decltype(MakeDefaultBlock2CTileMap(KGridDesc_N_K{}))>;
+
+    using ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3 = remove_cvref_t<decltype(
+        MakeCGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3(ZGridDesc_M_N{}))>;
+
+    // S / dP Gemm (type 1 rcc)
+    struct Gemm0
+    {
+        // A matrix in LDS memory, dst of blockwise copy
+        static constexpr auto a_block_desc_ak0_m_ak1 =
+            GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+
+        // B matrix in LDS memory, dst of blockwise copy
+        static constexpr auto b_block_desc_bk0_n_bk1 =
+            GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        template <typename ABlockDesc_AK0_M_AK1>
+        __host__ __device__ static constexpr auto
+        MakeGemm0AMmaTileDescriptor_M0_M1_M2_K(const ABlockDesc_AK0_M_AK1&)
+        {
+            constexpr index_t MWaves = MPerBlock / (MXdlPerWave * MPerXdl);
+
+            return MakeGemmMmaTileDescriptor_MN0_MN1_MN2_K<MXdlPerWave, MWaves, MPerXdl>(
+                ABlockDesc_AK0_M_AK1{});
+        }
+
+        template <typename BBlockDesc_BK0_N_BK1>
+        __host__ __device__ static constexpr auto
+        MakeGemm0BMmaTileDescriptor_N0_N1_N2_K(const BBlockDesc_BK0_N_BK1&)
+        {
+            constexpr index_t NWaves = NPerBlock / (NXdlPerWave * NPerXdl);
+
+            return MakeGemmMmaTileDescriptor_MN0_MN1_MN2_K<NXdlPerWave, NWaves, NPerXdl>(
+                BBlockDesc_BK0_N_BK1{});
+        }
+
+        template <typename GridDesc_K0_M_K1>
+        using ABlockwiseCopy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                AElementwiseOperation,
+                                                tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<AK0, MPerBlock, AK1>,
+                                                ABlockTransferThreadClusterLengths_AK0_M_AK1,
+                                                ABlockTransferThreadClusterArrangeOrder,
+                                                InputDataType,
+                                                GemmDataType,
+                                                GridDesc_K0_M_K1,
+                                                decltype(a_block_desc_ak0_m_ak1),
+                                                ABlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                ABlockTransferSrcVectorDim,
+                                                2,
+                                                ABlockTransferSrcScalarPerVector,
+                                                ABlockTransferDstScalarPerVector_AK1,
+                                                1,
+                                                1,
+                                                true, // SrcResetCoord
+                                                true, // DstResetCoord
+                                                NumGemmKPrefetchStage>;
+
+        template <typename GridDesc_K0_N_K1>
+        using BBlockwiseCopy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                BElementwiseOperation,
+                                                tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<BK0, NPerBlock, BK1>,
+                                                BBlockTransferThreadClusterLengths_BK0_N_BK1,
+                                                BBlockTransferThreadClusterArrangeOrder,
+                                                InputDataType,
+                                                GemmDataType,
+                                                GridDesc_K0_N_K1,
+                                                decltype(b_block_desc_bk0_n_bk1),
+                                                BBlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                BBlockTransferSrcVectorDim,
+                                                2,
+                                                BBlockTransferSrcScalarPerVector,
+                                                BBlockTransferDstScalarPerVector_BK1,
+                                                1,
+                                                1,
+                                                true, // SrcResetCoord
+                                                true, // DstResetCoord
+                                                NumGemmKPrefetchStage>;
+
+        static constexpr index_t KPack = math::max(math::lcm(AK1, BK1), mfma.k_per_blk);
+
+        // Blockwise gemm with transposed XDL output
+        using BlockwiseGemm = BlockwiseGemmXdlops_v2<
+            BlockSize,
+            GemmDataType,
+            FloatGemmAcc,
+            decltype(a_block_desc_ak0_m_ak1),
+            decltype(b_block_desc_bk0_n_bk1),
+            decltype(MakeGemm0AMmaTileDescriptor_M0_M1_M2_K(a_block_desc_ak0_m_ak1)),
+            decltype(MakeGemm0BMmaTileDescriptor_N0_N1_N2_K(b_block_desc_bk0_n_bk1)),
+            MPerBlock,
+            NPerBlock,
+            KPerBlock,
+            MPerXdl,
+            NPerXdl,
+            MXdlPerWave,
+            NXdlPerWave,
+            KPack>;
+
+        static constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
+        static constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1, 0, 0);
+    };
+
+    // dV / dK Gemm (type 2 rrr)
+    template <typename ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2,
+              typename ASrcBlockDesc_M0_N0_M1_N1_M2_M3_M4_N2>
+    struct Gemm1
+    {
+        private:
+        static constexpr auto m0 = ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2{}.GetLength(I0);
+        static constexpr auto n0 = ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2{}.GetLength(I1);
+        static constexpr auto m1 = ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2{}.GetLength(I2);
+        static constexpr auto n1 = ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2{}.GetLength(I3);
+        static constexpr auto m2 = ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2{}.GetLength(I4);
+        static constexpr auto m3 = ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2{}.GetLength(I5);
+        static constexpr auto m4 = ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2{}.GetLength(I6);
+        static constexpr auto n2 = ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2{}.GetLength(I7);
+
+        // M2 num_groups_per_blk, M3 num_input_blks, M4 group_size
+        static constexpr auto M3 = ASrcBlockDesc_M0_N0_M1_N1_M2_M3_M4_N2{}.GetLength(I5);
+
+        public:
+        static constexpr auto AThreadSliceLength_K0 = Number<Gemm1KPerBlock / m4 / M3>{};
+        static constexpr auto AThreadSliceLength_M  = Number<n0 * n1 * n2>{};
+        static constexpr auto AThreadSliceLength_K1 = Number<m4>{};
+
+        // A source matrix layout in AccVGPR
+        static constexpr auto a_src_thread_desc_k0_m_k1 =
+            GetA1SrcThreadDescriptor_AK0PerBlock_MPerBlock_AK1(
+                ASrcThreadDesc_M0_N0_M1_N1_M2_M3_M4_N2{});
+
+        // A matrix in VGPR memory, dst of AccVGPR-to-VGPR copy
+        static constexpr auto a_thread_desc_k0_m_k1 = make_naive_tensor_descriptor_packed(
+            make_tuple(AThreadSliceLength_K0, AThreadSliceLength_M, AThreadSliceLength_K1));
+
+        // B matrix in LDS memory, dst of blockwise copy
+        static constexpr auto b_block_desc_bk0_n_bk1 =
+            GetB1BlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        template <typename ABlockDesc_AK0_M_AK1>
+        __host__ __device__ static constexpr auto
+        MakeGemm1AMmaTileDescriptor_M0_M1_M2_K(const ABlockDesc_AK0_M_AK1&)
+        {
+            return MakeGemmMmaTileDescriptor_MN0_MN1_MN2_K<NXdlPerWave, 1, 1>(
+                ABlockDesc_AK0_M_AK1{});
+        }
+
+        template <typename BBlockDesc_BK0_N_BK1>
+        __host__ __device__ static constexpr auto
+        MakeGemm1BMmaTileDescriptor_N0_N1_N2_K(const BBlockDesc_BK0_N_BK1&)
+        {
+            constexpr index_t Gemm1NWaves = Gemm1NPerBlock / (Gemm1NXdlPerWave * NPerXdl);
+            return MakeGemmMmaTileDescriptor_MN0_MN1_MN2_K<Gemm1NXdlPerWave, Gemm1NWaves, NPerXdl>(
+                BBlockDesc_BK0_N_BK1{});
+        }
+
+        static constexpr auto ASrcScalarPerVector = m4;
+
+        using AThreadSliceLengths_K0_M_K1 = decltype(a_thread_desc_k0_m_k1.GetLengths());
+
+        template <typename ElementwiseOp = tensor_operation::element_wise::PassThrough>
+        using ABlockwiseCopy =
+            ThreadwiseTensorSliceTransfer_StaticToStatic<FloatGemmAcc,
+                                                         GemmDataType,
+                                                         decltype(a_src_thread_desc_k0_m_k1),
+                                                         decltype(a_thread_desc_k0_m_k1),
+                                                         ElementwiseOp,
+                                                         AThreadSliceLengths_K0_M_K1,
+                                                         Sequence<1, 0, 2>,
+                                                         2,
+                                                         ASrcScalarPerVector>;
+
+        template <typename GridDesc_K0_N_K1>
+        using BBlockwiseCopy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                BElementwiseOperation,
+                                                tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<B1K0, Gemm1NPerBlock, B1K1>,
+                                                B1BlockTransferThreadClusterLengths_BK0_N_BK1,
+                                                B1BlockTransferThreadClusterArrangeOrder,
+                                                InputDataType,
+                                                GemmDataType,
+                                                GridDesc_K0_N_K1,
+                                                decltype(b_block_desc_bk0_n_bk1),
+                                                B1BlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                B1BlockTransferSrcVectorDim,
+                                                2,
+                                                B1BlockTransferSrcScalarPerVector,
+                                                B1BlockTransferDstScalarPerVector_BK1,
+                                                1,
+                                                1,
+                                                B1ThreadTransferSrcResetCoordinateAfterRun,
+                                                true, // DstResetCoord
+                                                NumGemmKPrefetchStage>;
+
+        // for a_block_slice_copy_step to be able to address static buffers, it MUST be a
+        // tuple-based container as well as containing ONLY integral constants
+        static constexpr auto a_block_slice_copy_step = make_tuple(AThreadSliceLength_K0, I0, I0);
+        static constexpr auto b_block_slice_copy_step =
+            make_multi_index(Gemm1KPerBlock / B1K1, 0, 0);
+
+        // selected_mfma.group_size or B1K1 <= Gemm1KPack <= selected_mfma.group_size
+        // selected_mfma.k_per_blk <= Gemm1KPack
+        //
+        // Following similar rationale behind Gemm0KPack, let Gemm1KPack be the lowest common
+        // multiples of A1K1 (predetermined by selected_mfma.group_size) and B1K1. But in this case
+        // Gemm1KPack can't be higher than A1K1 itself because A1 matrix is distributed in VGPRs
+        // with 'group_size' amount of contiguous elements. Having Gemm1KPack greater than A1K1 will
+        // cause mismatch in summation index for example c[0:7] = a1[[0:3, 8:11]] * b1[0:7].
+        // therefore we may just as well assign Gemm1KPack = group_size
+        static constexpr index_t GemmKPack = mfma.group_size;
+
+        using BlockwiseGemm = BlockwiseGemmXdlops_v2<
+            BlockSize,
+            GemmDataType,
+            FloatGemmAcc,
+            decltype(a_thread_desc_k0_m_k1),
+            decltype(b_block_desc_bk0_n_bk1),
+            decltype(MakeGemm1AMmaTileDescriptor_M0_M1_M2_K(a_thread_desc_k0_m_k1)),
+            decltype(MakeGemm1BMmaTileDescriptor_N0_N1_N2_K(b_block_desc_bk0_n_bk1)),
+            NPerBlock,
+            Gemm1NPerBlock,
+            Gemm1KPerBlock,
+            MPerXdl,
+            NPerXdl,
+            NXdlPerWave,
+            Gemm1NXdlPerWave,
+            GemmKPack,
+            true,      // TransposeC
+            GemmKPack, // AMmaKStride
+            GemmKPack * XdlopsGemm<GemmDataType, MPerXdl, NPerXdl, GemmKPack, false>{}
+                            .K0PerXdlops /* BMmaKStride */>;
+    };
+
+    // dQ Gemm (type 3 crr)
+    // Describes tuning parameter for C2_m_n = A2_m_k * B2_k_n
+    struct Gemm2Params
+    {
+        static constexpr index_t Gemm2_M = MPerBlock;      // 64
+        static constexpr index_t Gemm2_K = NPerBlock;      // 128
+        static constexpr index_t Gemm2_N = Gemm1NPerBlock; // 128
+        static constexpr index_t Sum_K   = Gemm2KPerBlock;
+
+        static constexpr index_t A_K1     = 8; // dS will be row-major
+        static constexpr index_t A_K0     = Sum_K / A_K1;
+        static constexpr index_t A_LdsPad = 0; // how many multiples of K1 per M * K1 elements
+
+        static constexpr index_t B_K1     = B1K1; // dY assumed row-major, typically =2 for fp16
+        static constexpr index_t B_K0     = Sum_K / B_K1;
+        static constexpr index_t B_LdsPad = 0; // how many multiples of K1 per N * K1 elements
+
+        static_assert(Sum_K % NPerXdl == 0, "");
+
+        static constexpr index_t BSrcVectorDim       = 1; // Gemm2_N dimension
+        static constexpr index_t BSrcScalarPerVector = 4;
+
+        static constexpr index_t GemmNWave   = Gemm2_N / Gemm2NXdlPerWave / NPerXdl;    // 1 // 2
+        static constexpr index_t GemmMWave   = BlockSize / get_warp_size() / GemmNWave; // 4 // 2
+        static constexpr index_t GemmNRepeat = Gemm2NXdlPerWave;                        // 1 // 1
+        static constexpr index_t GemmMRepeat = Gemm2_M / GemmMWave / MPerXdl;           // 1 // 1
+        static constexpr index_t GemmKPack   = math::max(math::lcm(A_K1, B_K1), mfma.k_per_blk);
+
+        using BBlockSliceLengths = Sequence<B_K0, Gemm2_N, B_K1>;
+        using BThreadClusterLengths =
+            Sequence<BlockSize / (Gemm2_N / BSrcScalarPerVector), Gemm2_N / BSrcScalarPerVector, 1>;
+        using BThreadClusterArrangeOrder = Sequence<0, 2, 1>;
+
+        __host__ __device__ static constexpr auto GetABlockSliceLengths_M0_K0_M1_K1_M2_K2()
+        {
+            // perform manual unmerge: n -> n_repeat, n_waves, n_per_xdl
+            constexpr index_t k  = Sum_K - 1;
+            constexpr index_t k2 = k % NPerXdl;
+            constexpr index_t k1 = k / NPerXdl % Gemm0NWaves;
+            constexpr index_t k0 = k / NPerXdl / Gemm0NWaves % NXdlPerWave;
+
+            // perform manual unmerge: m -> m_repeat, m_waves, m_per_xdl
+            constexpr index_t m  = Gemm2_M - 1;
+            constexpr index_t m2 = m % MPerXdl;
+            constexpr index_t m1 = m / MPerXdl % Gemm0MWaves;
+            constexpr index_t m0 = m / MPerXdl / Gemm0MWaves % MXdlPerWave;
+
+            // assume 256 decomposed into 2 x 4 x 32
+            // 1d idx ( 32 - 1) -> 3d idx 0, 0, 31 -> 3d dim 1 x 1 x 32
+            // 1d idx (256 - 1) -> 3d idx 1, 3, 31 -> 3d dim 2 x 4 x 32
+            return Sequence<m0, k0, m1, k1, m2, k2>{} + Sequence<1, 1, 1, 1, 1, 1>{};
+        }
+
+        __host__ __device__ static constexpr auto GetABlockSliceLengths_M0_K0_M1_K1()
+        {
+            return generate_sequence_v2(
+                [](auto I) { return GetABlockSliceLengths_M0_K0_M1_K1_M2_K2().At(I); },
+                Number<4>{});
+        }
+
+        using ABlockSliceLengths_M0_K0_M1_K1 =
+            decltype(GetABlockSliceLengths_M0_K0_M1_K1()); //(2, 1, 1, 2) //(4, 1, 1, 2)
+    };
+
+    // dQ Gemm (type 3 crr)
+    template <typename Gemm2Params, typename ASrcBlockwiseGemm>
+    struct Gemm2
+    {
+        private:
+        static constexpr auto a_src_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
+            ASrcBlockwiseGemm::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+        static constexpr auto M0 = a_src_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I0); // repeat
+        static constexpr auto N0 = a_src_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I1);
+        static constexpr auto M1 = a_src_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I2); // wave
+        static constexpr auto N1 = a_src_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I3);
+        static constexpr auto M2 = a_src_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I4); // xdl
+        static constexpr auto M3 = a_src_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I5);
+        static constexpr auto M4 = a_src_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I6);
+        static constexpr auto N2 = a_src_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I7);
+
+        public:
+        // A source matrix layout in VGPR, src of VGPR-to-LDS copy
+        static constexpr auto a_src_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
+            ASrcBlockwiseGemm::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+        // A matrix in LDS memory, dst of blockwise copy
+        static constexpr auto a_block_desc_k0_m_k1 = GetA2BlockDescriptor_K0_M_K1<Gemm2Params>();
+
+        // B matrix in LDS memory, dst of blockwise copy
+        static constexpr auto b_block_desc_k0_n_k1 = GetB2BlockDescriptor_K0_N_K1<Gemm2Params>();
+
+        __host__ __device__ static constexpr auto MakeABlockDesc_M0_K0_M1_K1_M2_M3_M4_K2()
+        {
+            const auto K0_ = a_block_desc_k0_m_k1.GetLength(I0);
+            const auto M_  = a_block_desc_k0_m_k1.GetLength(I1);
+            const auto K1_ = a_block_desc_k0_m_k1.GetLength(I2);
+
+            const auto a_block_desc_m_k = transform_tensor_descriptor(
+                a_block_desc_k0_m_k1,
+                make_tuple(make_merge_transform_v3_division_mod(make_tuple(K0_, K1_)),
+                           make_pass_through_transform(M_)),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+            // HACK: for unmerge transform, the length of highest dim is irrelevant so we put dummy
+            // variable I1 there
+            return transform_tensor_descriptor(
+                a_block_desc_m_k,
+                make_tuple(make_unmerge_transform(make_tuple(I1, M1, M2, M3, M4)),
+                           make_unmerge_transform(make_tuple(I1, N1, N2))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2, 4, 5, 6>{}, Sequence<1, 3, 7>{}));
+        }
+
+        // Note: we will perform sub-workgroup VGPR-to-LDS copy to save LDS space, therefore the
+        // destination coordinate can overlap between wavefronts in a workgroup as seen in the mod
+        // operation before returning the values
+        __host__ __device__ static auto MakeAThreadOriginOnBlock_M0_K0_M1_K1_M2_M3_M4_K2()
+        {
+            const auto a_thread_origin_on_block_idx =
+                ASrcBlockwiseGemm::CalculateCThreadOriginDataIndex8D(I0, I0, I0, I0);
+
+            constexpr auto a_block_slice_lengths_m0_k0_m1_k1 =
+                typename Gemm2Params::ABlockSliceLengths_M0_K0_M1_K1{}; // mrepeat, nrepeat,
+                                                                        // mwaves, nwaves,
+
+            return make_tuple(
+                a_thread_origin_on_block_idx[I0],                                         // mrepeat
+                a_thread_origin_on_block_idx[I1],                                         // nrepeat
+                a_thread_origin_on_block_idx[I2] % a_block_slice_lengths_m0_k0_m1_k1[I2], // mwave
+                a_thread_origin_on_block_idx[I3] % a_block_slice_lengths_m0_k0_m1_k1[I3], // nwave
+                a_thread_origin_on_block_idx[I4],                                         // xdlops
+                a_thread_origin_on_block_idx[I5],
+                a_thread_origin_on_block_idx[I6],
+                a_thread_origin_on_block_idx[I7]);
+        }
+
+        static constexpr auto a_block_desc_m0_k0_m1_k1_m2_m3_m4_k2 =
+            MakeABlockDesc_M0_K0_M1_K1_M2_M3_M4_K2();
+
+        using ASrcBlockSliceWindowIterator =
+            SpaceFillingCurve<Sequence<M0, N0, M1, N1>,
+                              Sequence<0, 1, 2, 3>,
+                              typename Gemm2Params::ABlockSliceLengths_M0_K0_M1_K1,
+                              false>;
+
+        template <typename ElementwiseOp = tensor_operation::element_wise::PassThrough>
+        using ABlockwiseCopy = ThreadwiseTensorSliceTransfer_v1r3<
+            FloatGemmAcc,
+            GemmDataType,
+            decltype(a_src_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+            decltype(a_block_desc_m0_k0_m1_k1_m2_m3_m4_k2),
+            ElementwiseOp,
+            Sequence<Gemm2Params::ABlockSliceLengths_M0_K0_M1_K1::At(I0), // ThreadSliceLengths
+                     Gemm2Params::ABlockSliceLengths_M0_K0_M1_K1::At(I1),
+                     I1,
+                     I1,
+                     M2,
+                     I1,
+                     M4,
+                     I1>,
+            Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+            7, // DstVectorDim
+            1, // DstScalarPerVector
+            InMemoryDataOperationEnum::Set,
+            1, // DstScalarStrideInVector
+            true>;
+
+        template <typename GridDesc_K0_N_K1>
+        using BBlockwiseCopy = ThreadGroupTensorSliceTransfer_v4r1<
+            ThisThreadBlock,
+            tensor_operation::element_wise::PassThrough,
+            tensor_operation::element_wise::PassThrough,
+            InMemoryDataOperationEnum::Set,
+            typename Gemm2Params::BBlockSliceLengths,
+            typename Gemm2Params::BThreadClusterLengths,
+            typename Gemm2Params::BThreadClusterArrangeOrder,
+            InputDataType,
+            GemmDataType,
+            GridDesc_K0_N_K1,
+            decltype(b_block_desc_k0_n_k1),
+            typename Gemm2Params::BThreadClusterArrangeOrder, // access order == thread order
+            Sequence<1, 0, 2>,
+            Gemm2Params::BSrcVectorDim,
+            2, // DstVectorDim
+            Gemm2Params::BSrcScalarPerVector,
+            Gemm2Params::B_K1,
+            1,
+            1,
+            true,
+            true,
+            1>;
+
+        using BlockwiseGemm =
+            BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
+                                                                GemmDataType,
+                                                                FloatGemmAcc,
+                                                                decltype(a_block_desc_k0_m_k1),
+                                                                decltype(b_block_desc_k0_n_k1),
+                                                                MPerXdl,
+                                                                NPerXdl,
+                                                                Gemm2Params::GemmMRepeat,
+                                                                Gemm2Params::GemmNRepeat,
+                                                                Gemm2Params::GemmKPack,
+                                                                true>; // TranspossC
+
+        static constexpr auto b_block_slice_copy_step = make_multi_index(Gemm2Params::B_K0, 0, 0);
+        static constexpr auto c_block_slice_copy_step =
+            make_multi_index(-Gemm2Params::GemmMRepeat, 0, 0, 0, 0, 0, 0, 0);
+        static constexpr auto b_block_reset_copy_step =
+            make_multi_index(-NPerBlock / Gemm2Params::B_K1, 0, 0);
+
+        template <typename CGradDesc_M_N>
+        __host__ __device__ static auto
+        MakeCGridDesc_M0_N0_M1_N1_M2_N2_N3_N4(const CGradDesc_M_N& c_grid_desc_m_n)
+        {
+            // HACK: for unmerge transform, the length of highest dim is irrelevant so we put dummy
+            // variable I1 there
+            const auto c_grid_desc_m0_n0_m1_n1_m2_n2 = transform_tensor_descriptor(
+                c_grid_desc_m_n,
+                make_tuple(make_unmerge_transform(make_tuple(I1, Gemm2Params::GemmMWave, MPerXdl)),
+                           make_unmerge_transform(make_tuple(I1, Gemm2Params::GemmNWave, NPerXdl))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}));
+
+            const auto c_grid_desc_m0_n0_m1_n1_m2_n2_n3_n4 =
+                BlockwiseGemm{}.xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_N2_N3_N4(
+                    c_grid_desc_m0_n0_m1_n1_m2_n2);
+
+            return c_grid_desc_m0_n0_m1_n1_m2_n2_n3_n4;
+        }
+
+        static constexpr auto c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4 =
+            BlockwiseGemm::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4();
+
+        __host__ __device__ static auto GetCThreadOriginOnBlock_M0_N0_M1_N1_M2_N2_N3_N4()
+        {
+            return to_multi_index(BlockwiseGemm::CalculateCThreadOriginDataIndex8D(I0, I0, I0, I0));
+        }
+
+        template <typename CGridDesc_M0_N0_M1_N1_M2_N2_N3_N4,
+                  typename ElementwiseOp = tensor_operation::element_wise::PassThrough>
+        using CBlockwiseCopy = ThreadwiseTensorSliceTransfer_v1r3<
+            FloatGemmAcc,
+            OutputDataType,
+            decltype(c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4),
+            CGridDesc_M0_N0_M1_N1_M2_N2_N3_N4,
+            ElementwiseOp,                                                // CElementwiseOperation
+            decltype(c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4.GetLengths()), // SliceLengths
+            Sequence<0, 1, 2, 3, 4, 5, 6, 7>,                             // AccessOrder
+            7,                                                            // VectorDim
+            2,                                                            // ScalarPerVector
+            InMemoryDataOperationEnum::AtomicAdd, // GlobalMemoryDataOperation
+            1,                                    // DstScalarStrideInVector
+            true>;
+    };
+
+    template <index_t BlockSize_, index_t BlockSliceLength_M_, index_t BlockSliceLength_O_>
+    struct YDotYGrad_M_O_
+    {
+        static constexpr index_t SrcScalarPerVector = 16 / sizeof(InputDataType);
+        static constexpr auto ThreadClusterLength_O =
+            Number<BlockSliceLength_O_ / SrcScalarPerVector>{};
+        static constexpr auto ThreadClusterLength_M = Number<BlockSize_ / ThreadClusterLength_O>{};
+        static constexpr auto ThreadSliceLength_O   = Number<SrcScalarPerVector>{};
+        static constexpr auto ThreadSliceLength_M =
+            Number<BlockSliceLength_M_ * ThreadClusterLength_O / BlockSize_>{};
+
+        static_assert(ThreadClusterLength_O * ThreadSliceLength_O == BlockSliceLength_O_, "");
+        static_assert(ThreadClusterLength_M * ThreadSliceLength_M == BlockSliceLength_M_, "");
+
+        using SrcBufType = StaticBuffer<AddressSpaceEnum::Vgpr,
+                                        FloatGemmAcc,
+                                        ThreadSliceLength_M * ThreadSliceLength_O,
+                                        true>;
+
+        using DstBufType =
+            StaticBuffer<AddressSpaceEnum::Vgpr, FloatGemmAcc, ThreadSliceLength_M, true>;
+    };
+    using YDotYGrad_M_O = YDotYGrad_M_O_<BlockSize, MPerBlock, Gemm1NPerBlock>;
+
+    // PGrad Gemm has the same layout as P = Q * K^T Gemm (A row-major B col-major)
+    struct PGradGemmTile_M_N_O
+    {
+        // TODO:
+        // Make all input tensors 2D and transform them into appropriate 3D form in kernel to make
+        // things more concise
+        template <typename YGradGridDesc_M0_O_M1_>
+        __device__ static auto
+        MakeYGradGridDesc_O0_M_O1(const YGradGridDesc_M0_O_M1_& ygrad_grid_desc_m0_o_m1)
+        {
+            const auto M0 = ygrad_grid_desc_m0_o_m1.GetLength(I0);
+            const auto O  = ygrad_grid_desc_m0_o_m1.GetLength(I1);
+            const auto M1 = ygrad_grid_desc_m0_o_m1.GetLength(I2);
+
+            constexpr auto Y_O1 = AK1;
+            const auto Y_O0     = O / Y_O1;
+
+            const auto ygrad_grid_desc_o0_m_o1 = transform_tensor_descriptor(
+                ygrad_grid_desc_m0_o_m1,
+                make_tuple(make_unmerge_transform(make_tuple(Y_O0, Y_O1)),
+                           make_merge_transform_v3_division_mod(make_tuple(M0, M1))),
+                make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return ygrad_grid_desc_o0_m_o1;
+        }
+
+        template <typename VGridDesc_N0_O_N1_>
+        __device__ static auto MakeVGridDesc_O0_N_O1(const VGridDesc_N0_O_N1_& v_grid_desc_n0_o_n1)
+        {
+            const auto N0 = v_grid_desc_n0_o_n1.GetLength(I0);
+            const auto O  = v_grid_desc_n0_o_n1.GetLength(I1);
+            const auto N1 = v_grid_desc_n0_o_n1.GetLength(I2);
+
+            constexpr auto V_O1 = BK1;
+            const auto V_O0     = O / V_O1;
+
+            const auto v_grid_desc_o0_n_o1 = transform_tensor_descriptor(
+                v_grid_desc_n0_o_n1,
+                make_tuple(make_unmerge_transform(make_tuple(V_O0, V_O1)),
+                           make_merge_transform_v3_division_mod(make_tuple(N0, N1))),
+                make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return v_grid_desc_o0_n_o1;
+        }
+    };
+
+    // QGrad Gemm has the same layout as Y = P * V Gemm (A in acc B row-major)
+    struct QGradGemmTile_M_K_N
+    {
+        template <typename KGridDesc_K0_N_K1_>
+        __device__ static auto MakeKGridDesc_N0_K_N1(const KGridDesc_K0_N_K1_& k_grid_desc_k0_n_k1)
+        {
+            const auto K_K0 = k_grid_desc_k0_n_k1.GetLength(I0);
+            const auto N    = k_grid_desc_k0_n_k1.GetLength(I1);
+            const auto K_K1 = k_grid_desc_k0_n_k1.GetLength(I2);
+
+            constexpr auto K_N1 = B1K1;
+            const auto K_N0     = N / K_N1;
+
+            const auto k_grid_desc_n0_k_n1 = transform_tensor_descriptor(
+                k_grid_desc_k0_n_k1,
+                make_tuple(make_unmerge_transform(make_tuple(K_N0, K_N1)),
+                           make_merge_transform_v3_division_mod(make_tuple(K_K0, K_K1))),
+                make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return k_grid_desc_n0_k_n1;
+        }
+    };
+
+    struct KGradGemmTile_N_K_M
+    {
+        // B position
+        template <typename QGridDesc_K0_M_K1_>
+        __device__ static auto MakeQGridDesc_M0_K_M1(const QGridDesc_K0_M_K1_& q_grid_desc_k0_m_k1)
+        {
+            const auto Q_K0 = q_grid_desc_k0_m_k1.GetLength(I0);
+            const auto M    = q_grid_desc_k0_m_k1.GetLength(I1);
+            const auto Q_K1 = q_grid_desc_k0_m_k1.GetLength(I2);
+
+            constexpr auto Q_M1 = B1K1;
+            const auto Q_M0     = M / Q_M1;
+
+            const auto q_grid_desc_m0_k_m1 = transform_tensor_descriptor(
+                q_grid_desc_k0_m_k1,
+                make_tuple(make_unmerge_transform(make_tuple(Q_M0, Q_M1)),
+                           make_merge_transform_v3_division_mod(make_tuple(Q_K0, Q_K1))),
+                make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return q_grid_desc_m0_k_m1;
+        }
+    };
+
+    // D0
+    static constexpr auto D0M2 = Number<4>{};
+    static constexpr auto D0M1 = Number<MPerXdl>{} / D0M2;
+    static constexpr auto D0M0 = Number<MPerBlock>{} / Number<MPerXdl>{};
+
+    __host__ __device__ static constexpr auto
+    MakeD0GridDescriptor_M0_N0_M1_M2_N1_M3(const D0GridDesc_M_N& d0_grid_desc_m_n)
+    {
+        const auto M = d0_grid_desc_m_n.GetLength(I0);
+        const auto N = d0_grid_desc_m_n.GetLength(I1);
+
+        const auto MBlock = M / MPerBlock;
+        const auto NBlock = N / NPerBlock;
+
+        const auto d0_grid_desc_m0_n0_m1_m2_n1_m3 = transform_tensor_descriptor(
+            d0_grid_desc_m_n,
+            make_tuple(make_unmerge_transform(make_tuple(MBlock, D0M0, D0M1, D0M2)),
+                       make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 2, 3, 5>{}, Sequence<1, 4>{}));
+
+        return d0_grid_desc_m0_n0_m1_m2_n1_m3;
+    }
+
+    using D0GridDescriptor_M0_N0_M1_M2_N1_M3 =
+        remove_cvref_t<decltype(MakeD0GridDescriptor_M0_N0_M1_M2_N1_M3(D0GridDesc_M_N{}))>;
+
+    struct D0Operator
+    {
+        template <typename DataType>
+        struct TypeTransform
+        {
+            using Type                     = DataType;
+            static constexpr index_t Size0 = sizeof(DataType);
+            static constexpr index_t Size  = sizeof(DataType);
+        };
+        template <>
+        struct TypeTransform<void>
+        {
+            using Type                     = ck::half_t;
+            static constexpr index_t Size0 = 0;
+            static constexpr index_t Size  = sizeof(ck::half_t);
+        };
+        static constexpr index_t NThreadClusterLengths = 32;
+        static_assert(NPerXdl == 32);
+        static_assert(D0BlockTransferSrcScalarPerVector * NThreadClusterLengths <= NPerBlock,
+                      "D0BlockTransferSrcScalarPerVector * NThreadClusterLengths <= NPerBlock");
+        __host__ __device__ static constexpr auto GetD0BlockGlobalDescriptor_M0_N0_M1_M2_N1_M3()
+        {
+            return make_naive_tensor_descriptor_packed(
+                make_tuple(I1, I1, I1, D0M1, Number<NPerBlock>{}, D0M2));
+        }
+        __host__ __device__ static constexpr auto GetD0BlockVgprDescriptor_N0_N1_M0_M1_M2()
+        {
+            constexpr auto d0_raw_m0_n_m1 =
+                make_naive_tensor_descriptor_packed(make_tuple(D0M1, Number<NPerBlock>{}, D0M2));
+
+            constexpr auto d0_n0_n1_m0_m1_m2 = transform_tensor_descriptor(
+                d0_raw_m0_n_m1,
+                make_tuple(make_unmerge_transform(make_tuple(D0M1 / I2, I2)),
+                           make_unmerge_transform(
+                               make_tuple(Number<NPerBlock / NPerXdl>{}, Number<NPerXdl>{})),
+                           make_pass_through_transform(D0M2)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<2, 3>{}, Sequence<0, 1>{}, Sequence<4>{}));
+            return d0_n0_n1_m0_m1_m2;
+        }
+        static constexpr auto d0_block_dst_desc_m0_n0_m1_m2_n1_m3 =
+            GetD0BlockGlobalDescriptor_M0_N0_M1_M2_N1_M3();
+        static constexpr auto d0_block_src_desc_n0_n1_m0_m1_m2 =
+            GetD0BlockVgprDescriptor_N0_N1_M0_M1_M2();
+
+        static constexpr auto d0_thread_desc_ =
+            make_naive_tensor_descriptor_packed(make_tuple(I1, I1, I4, I1, D0M2));
+
+        static constexpr auto& d0grad_block_dst_desc_n0_n1_m0_m1_m2 =
+            d0_block_src_desc_n0_n1_m0_m1_m2;
+        static constexpr auto& d0grad_block_src_desc_m0_n0_m1_m2_n1_m3 =
+            d0_block_dst_desc_m0_n0_m1_m2_n1_m3;
+
+        using D0BlockwiseCopyGlobalToLds = ThreadGroupTensorSliceTransfer_v4r1<
+            ThisThreadBlock,
+            tensor_operation::element_wise::PassThrough,
+            tensor_operation::element_wise::PassThrough,
+            InMemoryDataOperationEnum::Set,
+            Sequence<I1, I1, I1, D0M1, NPerBlock, D0M2>, // BlockSliceLengths
+            Sequence<1,
+                     1,
+                     1,
+                     BlockSize / NThreadClusterLengths,
+                     NThreadClusterLengths,
+                     1>,                                   // ThreadClusterLengths
+            Sequence<0, 1, 2, 3, 5, 4>,                    // ThreadClusterArrangeOrder
+            typename TypeTransform<D0DataType>::Type,      // SrcData
+            typename TypeTransform<D0DataType>::Type,      // DstData
+            D0GridDescriptor_M0_N0_M1_M2_N1_M3,            // SrcDesc
+            decltype(d0_block_dst_desc_m0_n0_m1_m2_n1_m3), // DstDesc
+            Sequence<0, 1, 2, 3, 5, 4>,                    // SrcDimAccessOrder
+            Sequence<0, 1, 2, 4, 3, 5>,                    // DstDimAccessOrder
+            4,                                             // SrcVectorDim
+            5,                                             // DstVectorDim
+            4,                                             // SrcScalarPerVector
+            4,                                             // DstScalarPerVector
+            1,
+            1,
+            true,
+            true, // DstResetCoord
+            1>;
+
+        using D0ThreadwiseCopyLdsToVgpr =
+            ThreadwiseTensorSliceTransfer_v4<typename TypeTransform<D0DataType>::Type,   // SrcData
+                                             typename TypeTransform<D0DataType>::Type,   // DstData
+                                             decltype(d0_block_src_desc_n0_n1_m0_m1_m2), // SrcDesc
+                                             decltype(d0_thread_desc_),                  // DstDesc
+                                             Sequence<1, 1, 4, 1, 4>, // SliceLengths
+                                             Sequence<0, 1, 2, 3, 4>, // DimAccessOrder
+                                             4,                       // SrcVectorDim
+                                             4,                       // SrcScalarPerVector
+                                             2>;
+
+        using D0GradThreadwiseCopyVgprToLds = ThreadwiseTensorSliceTransfer_v1r3<
+            FloatGemmAcc,
+            typename TypeTransform<D0DataType>::Type,
+            decltype(d0_thread_desc_),
+            decltype(d0grad_block_dst_desc_n0_n1_m0_m1_m2),
+            tensor_operation::element_wise::Scale, // CElementwiseOperation
+            Sequence<1, 1, 4, 1, 4>,               // SliceLengths
+            Sequence<0, 1, 2, 3, 4>,               // AccessOrder
+            4,                                     // VectorDim
+            4,                                     // ScalarPerVector
+            InMemoryDataOperationEnum::Set,        // GlobalMemoryDataOperation
+            1,                                     // DstScalarStrideInVector
+            true>;
+
+        using D0GradBlockwiseCopyLdsToGlobal = ThreadGroupTensorSliceTransfer_v4r1<
+            ThisThreadBlock,
+            tensor_operation::element_wise::PassThrough,
+            tensor_operation::element_wise::PassThrough,
+            InMemoryDataOperationEnum::Set,
+            Sequence<I1, I1, I1, D0M1, NPerBlock, D0M2>, // BlockSliceLengths
+            Sequence<1,
+                     1,
+                     1,
+                     BlockSize / NThreadClusterLengths,
+                     NThreadClusterLengths,
+                     1>,                                       // ThreadClusterLengths
+            Sequence<0, 1, 2, 3, 5, 4>,                        // ThreadClusterArrangeOrder
+            typename TypeTransform<D0DataType>::Type,          // SrcData
+            typename TypeTransform<D0DataType>::Type,          // DstData
+            decltype(d0grad_block_src_desc_m0_n0_m1_m2_n1_m3), // SrcDesc
+            D0GridDescriptor_M0_N0_M1_M2_N1_M3,                // DstDesc
+            Sequence<0, 1, 2, 4, 3, 5>,                        // SrcDimAccessOrder
+            Sequence<0, 1, 2, 3, 5, 4>,                        // DstDimAccessOrder
+            5,                                                 // SrcVectorDim
+            4,                                                 // DstVectorDim
+            4,                                                 // SrcScalarPerVector
+            D0BlockTransferSrcScalarPerVector,                 // DstScalarPerVector
+            1,
+            1,
+            true,
+            true, // DstResetCoord
+            1>;
+    };
+
+    struct SharedMemTrait
+    {
+        // LDS allocation for A and B: be careful of alignment
+        static constexpr auto a_block_desc_ak0_m_ak1 =
+            GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+        static constexpr auto b_block_desc_bk0_n_bk1 =
+            GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+        static constexpr auto b1_block_desc_bk0_n_bk1 =
+            GetB1BlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+        static constexpr auto a2_block_desc_k0_m_k1 = GetA2BlockDescriptor_K0_M_K1<Gemm2Params>();
+        static constexpr auto b2_block_desc_k0_n_k1 = GetB2BlockDescriptor_K0_N_K1<Gemm2Params>();
+
+        static constexpr auto max_lds_align = Number<16 / sizeof(GemmDataType)>{};
+
+        static constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
+        static constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
+        static constexpr auto b1_block_space_size_aligned = math::integer_least_multiple(
+            b1_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
+        static constexpr auto a2_block_space_size_aligned = math::integer_least_multiple(
+            a2_block_desc_k0_m_k1.GetElementSpaceSize(), max_lds_align);
+        static constexpr auto b2_block_space_size_aligned = math::integer_least_multiple(
+            b2_block_desc_k0_n_k1.GetElementSpaceSize(), max_lds_align);
+
+        static constexpr auto a_block_space_offset  = 0;
+        static constexpr auto b_block_space_offset  = a_block_space_size_aligned.value;
+        static constexpr auto b1_block_space_offset = 0;
+        static constexpr auto a2_block_space_offset = 0;
+        static constexpr auto b2_block_space_offset = a2_block_space_size_aligned.value;
+
+        // LDS allocation for reduction
+        static constexpr index_t reduction_space_size_aligned =
+            math::integer_least_multiple(BlockSize, max_lds_align);
+
+        static constexpr auto reduction_space_offset = 0;
+
+        static constexpr auto d0_block_space_size_aligned = math::integer_least_multiple(
+            D0Operator::d0_block_dst_desc_m0_n0_m1_m2_n1_m3.GetElementSpaceSize(), max_lds_align);
+        static constexpr auto d0_block_space_offset = 0;
+
+        // LDS allocation for C shuffle in LDS
+        static constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+        static constexpr auto c_block_space_size =
+            c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
+    };
+
+    __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
+    {
+        const index_t gemm0_bytes_end = (SharedMemTrait::a_block_space_size_aligned +
+                                         SharedMemTrait::b_block_space_size_aligned) *
+                                        sizeof(GemmDataType);
+        const index_t gemm1_bytes_end =
+            (SharedMemTrait::b1_block_space_offset + SharedMemTrait::b1_block_space_size_aligned) *
+            sizeof(GemmDataType);
+        const index_t gemm2_bytes_end = (SharedMemTrait::a2_block_space_size_aligned +
+                                         SharedMemTrait::b2_block_space_size_aligned) *
+                                        sizeof(GemmDataType);
+
+        const index_t softmax_bytes_end = (SharedMemTrait::reduction_space_offset +
+                                           SharedMemTrait::reduction_space_size_aligned) *
+                                          sizeof(FloatGemmAcc);
+        const index_t d0_bytes_end =
+            (SharedMemTrait::d0_block_space_offset + SharedMemTrait::d0_block_space_size_aligned) *
+            D0Operator::template TypeTransform<D0DataType>::Size0;
+        const index_t c_block_bytes_end =
+            SharedMemTrait::c_block_space_size * sizeof(FloatCShuffle);
+
+        return math::max(gemm0_bytes_end,
+                         gemm1_bytes_end,
+                         gemm2_bytes_end,
+                         softmax_bytes_end,
+                         d0_bytes_end,
+                         c_block_bytes_end);
+    }
+
+    template <bool HasMainKBlockLoop,
+              bool IsDropout,
+              typename Block2CTileMap,
+              typename C0MatrixMask,
+              typename YGradGridDesc_M0_O_M1>
+    __device__ static void
+    Run(const InputDataType* __restrict__ p_q_grid,
+        const InputDataType* __restrict__ p_k_grid,
+        const D0DataType* __restrict__ p_d0_grid,
+        ZDataType* __restrict__ p_z_grid,
+        const InputDataType* __restrict__ p_v_grid,
+        const InputDataType* __restrict__ p_y_grid,
+        const FloatLSE* __restrict__ p_lse_grid,
+        const InputDataType* __restrict__ p_ygrad_grid,
+        OutputDataType* __restrict__ p_qgrad_grid,
+        OutputDataType* __restrict__ p_kgrad_grid,
+        D0DataType* __restrict__ p_d0grad_grid,
+        OutputDataType* __restrict__ p_vgrad_grid,
+        void* __restrict__ p_shared,
+        const AElementwiseOperation& a_element_op,
+        const BElementwiseOperation& b_element_op,
+        const SElementwiseOperation& s_element_op,
+        const B1ElementwiseOperation& b1_element_op,
+        const CElementwiseOperation& c_element_op,
+        const QGridDesc_K0_M_K1& q_grid_desc_k0_m_k1,
+        const KGridDesc_K0_N_K1& k_grid_desc_k0_n_k1,
+        const KGridDesc_K0_N_K1& kgrad_grid_desc_k0_n_k1,
+        const D0GridDescriptor_M0_N0_M1_M2_N1_M3& d0_grid_desc_m0_n0_m1_m2_n1_m3,
+        const ZGridDescriptor_M0_N0_M1_N1_M2_N2_M3_M4_M5_N3&
+            z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+        const VGridDesc_N0_O_N1& v_grid_desc_n0_o_n1,
+        const VGridDesc_N0_O_N1& vgrad_grid_desc_n0_o_n1,
+        const YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock&
+            y_grid_desc_mblock_mperblock_oblock_operblock,
+        const LSEGridDesc_M& lse_grid_desc_m,
+        const YGradGridDesc_M0_O_M1& ygrad_grid_desc_m0_o_m1,
+        const Block2CTileMap& block_2_ctile_map,
+        const C0MatrixMask& c0_matrix_mask,
+        const float p_drop,
+        ck::philox& ph,
+        const index_t z_random_matrix_offset,
+        const index_t raw_n_padded,
+        const index_t block_idx_n)
+    {
+        const FloatGemmAcc p_dropout  = type_convert<FloatGemmAcc>(1.0f - p_drop);
+        const FloatGemmAcc rp_dropout = type_convert<FloatGemmAcc>(1.0f / p_dropout);
+        const uint8_t p_dropout_in_uint8_t =
+            __builtin_amdgcn_readfirstlane(uint8_t(std::floor(p_dropout * 255.0)));
+        const tensor_operation::element_wise::Scale scale_rp_dropout(s_element_op.Value() *
+                                                                     rp_dropout);
+
+        const auto q_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_q_grid, q_grid_desc_k0_m_k1.GetElementSpaceSize());
+        const auto k_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_k_grid, k_grid_desc_k0_n_k1.GetElementSpaceSize());
+        const auto v_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_v_grid, v_grid_desc_n0_o_n1.GetElementSpaceSize());
+        const auto y_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_y_grid, y_grid_desc_mblock_mperblock_oblock_operblock.GetElementSpaceSize());
+        const auto lse_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_lse_grid, lse_grid_desc_m.GetElementSpaceSize());
+        const auto ygrad_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_ygrad_grid, ygrad_grid_desc_m0_o_m1.GetElementSpaceSize());
+        auto vgrad_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_vgrad_grid, vgrad_grid_desc_n0_o_n1.GetElementSpaceSize());
+        auto qgrad_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_qgrad_grid, q_grid_desc_k0_m_k1.GetElementSpaceSize());
+        auto kgrad_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_kgrad_grid, kgrad_grid_desc_k0_n_k1.GetElementSpaceSize());
+
+        // divide block work by [N, K]
+        const auto block_work_idx =
+            block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
+
+        const index_t block_work_idx_n = Deterministic ? block_idx_n : block_work_idx[I0];
+
+        // HACK: this force n_block_data_idx_on_grid into SGPR
+        const index_t n_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_work_idx_n * NPerBlock);
+
+        const index_t num_gemm0_m_block_outer_loop = q_grid_desc_k0_m_k1.GetLength(I1) / MPerBlock;
+        constexpr index_t num_gemm1_k_block_inner_loop = MPerBlock / Gemm1KPerBlock;
+
+        // 6 GEMM operations are categorized into 3 buckets. SizeK == SizeO == head_dim
+        // S_MNK  / dP_MNO Gemm (Gemm0 rcc)
+        // dV_NOM / dK_NKM Gemm (Gemm1 rrr)
+        // Y_MON  / dQ_MKN Gemm (Gemm2 crr)
+
+        //
+        // set up S / dP Gemm (type 1 rcc)
+        //
+
+        // Gemm0: LDS allocation for A and B: be careful of alignment
+        auto gemm0_a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<GemmDataType*>(p_shared) + SharedMemTrait::a_block_space_offset,
+            Gemm0::a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+
+        auto gemm0_b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<GemmDataType*>(p_shared) + SharedMemTrait::b_block_space_offset,
+            Gemm0::b_block_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        // Gemm0: gridwise GEMM pipeline
+        // Only supports LoopScheduler::Default
+        const auto gemm0_gridwise_gemm_pipeline =
+            GridwiseGemmPipeline_Selector<PipelineVer,
+                                          NumGemmKPrefetchStage,
+                                          LoopScheduler::Default>();
+        // S: A matrix blockwise copy
+        auto s_gemm_tile_q_blockwise_copy =
+            typename Gemm0::template ABlockwiseCopy<decltype(q_grid_desc_k0_m_k1)>(
+                q_grid_desc_k0_m_k1,
+                make_multi_index(0,
+                                 MPerBlock * (num_gemm0_m_block_outer_loop - 1),
+                                 0), // will loop over GemmM dimension
+                a_element_op,
+                Gemm0::a_block_desc_ak0_m_ak1,
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        // S: B matrix blockwise copy
+        auto s_gemm_tile_k_blockwise_copy =
+            typename Gemm0::template BBlockwiseCopy<decltype(k_grid_desc_k0_n_k1)>(
+                k_grid_desc_k0_n_k1,
+                make_multi_index(0, n_block_data_idx_on_grid, 0),
+                b_element_op,
+                Gemm0::b_block_desc_bk0_n_bk1,
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        // S: blockwise gemm
+        auto s_blockwise_gemm = typename Gemm0::BlockwiseGemm{}; // TransposeC
+
+        auto s_slash_p_thread_buf = s_blockwise_gemm.GetCThreadBuffer();
+
+        const auto s_gemm_tile_q_block_reset_copy_step =
+            make_multi_index(-q_grid_desc_k0_m_k1.GetLength(I0), -MPerBlock, 0);
+        const auto s_gemm_tile_k_block_reset_copy_step =
+            make_multi_index(-k_grid_desc_k0_n_k1.GetLength(I0), 0, 0);
+
+        const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
+            (q_grid_desc_k0_m_k1.GetLength(I0) * q_grid_desc_k0_m_k1.GetLength(I2)) / KPerBlock);
+
+        // dP: transform input and output tensor descriptors
+        const auto ygrad_grid_desc_o0_m_o1 =
+            PGradGemmTile_M_N_O::MakeYGradGridDesc_O0_M_O1(ygrad_grid_desc_m0_o_m1);
+        const auto v_grid_desc_o0_n_o1 =
+            PGradGemmTile_M_N_O::MakeVGridDesc_O0_N_O1(v_grid_desc_n0_o_n1);
+
+        // dP: A matrix blockwise copy
+        auto pgrad_gemm_tile_ygrad_blockwise_copy =
+            typename Gemm0::template ABlockwiseCopy<decltype(ygrad_grid_desc_o0_m_o1)>(
+                ygrad_grid_desc_o0_m_o1,
+                make_multi_index(0,
+                                 MPerBlock * (num_gemm0_m_block_outer_loop - 1),
+                                 0), // will loop over GemmM dimension
+                tensor_operation::element_wise::PassThrough{},
+                Gemm0::a_block_desc_ak0_m_ak1,
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        // dP: B matrix blockwise copy
+        auto pgrad_gemm_tile_v_blockwise_copy =
+            typename Gemm0::template BBlockwiseCopy<decltype(v_grid_desc_o0_n_o1)>(
+                v_grid_desc_o0_n_o1,
+                make_multi_index(0, n_block_data_idx_on_grid, 0),
+                tensor_operation::element_wise::PassThrough{},
+                Gemm0::b_block_desc_bk0_n_bk1,
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        // dP: blockwise gemm
+        // we need separate blockwise gemm object because we need separate thread buffer
+        auto pgrad_blockwise_gemm = typename Gemm0::BlockwiseGemm{};
+
+        auto pgrad_thread_buf = pgrad_blockwise_gemm.GetCThreadBuffer();
+
+        const auto pgrad_gemm_tile_ygrad_block_reset_copy_step =
+            make_multi_index(-ygrad_grid_desc_o0_m_o1.GetLength(I0), -MPerBlock, 0);
+        const auto pgrad_gemm_tile_v_block_reset_copy_step =
+            make_multi_index(-v_grid_desc_o0_n_o1.GetLength(I0), 0, 0);
+
+        const index_t num_o_block_main_loop = __builtin_amdgcn_readfirstlane(
+            (ygrad_grid_desc_o0_m_o1.GetLength(I0) * ygrad_grid_desc_o0_m_o1.GetLength(I2)) /
+            KPerBlock);
+
+        //
+        // set up dV / dK Gemm (type 2 rrr)
+        //
+        using Gemm1 =
+            Gemm1<decltype(s_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()),
+                  decltype(s_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2())>;
+
+        // Gemm1: VGPR allocation for A and LDS allocation for B
+        auto gemm1_a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, GemmDataType>(
+            Gemm1::a_thread_desc_k0_m_k1.GetElementSpaceSize());
+
+        auto gemm1_b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<GemmDataType*>(p_shared) + SharedMemTrait::b1_block_space_offset,
+            Gemm1::b_block_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        // dV: A matrix blockwise copy
+        auto vgrad_gemm_tile_p_blockwise_copy =
+            typename Gemm1::template ABlockwiseCopy<tensor_operation::element_wise::Relu>{
+                tensor_operation::element_wise::Relu{}}; // relu(P-dropped)
+
+        // dV: B matrix blockwise copy
+        auto vgrad_gemm_tile_ygrad_blockwise_copy =
+            typename Gemm1::template BBlockwiseCopy<decltype(ygrad_grid_desc_m0_o_m1)>(
+                ygrad_grid_desc_m0_o_m1,
+                make_multi_index(MPerBlock / B1K1 * (num_gemm0_m_block_outer_loop - 1), 0, 0),
+                b1_element_op,
+                Gemm1::b_block_desc_bk0_n_bk1, // there n actually is k, k is N, so name can be
+                                               // b_block_desc_bn0_k_bn1
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        const auto vgrad_gemm_tile_ygrad_block_next_copy_step =
+            make_multi_index(-2 * MPerBlock / B1K1, 0, 0);
+
+        // dV: blockwise gemm
+        auto vgrad_blockwise_gemm =
+            typename Gemm1::BlockwiseGemm{make_tuple(0, 0, 0, 0)}; // A_origin
+
+        auto vgrad_thread_buf = vgrad_blockwise_gemm.GetCThreadBuffer();
+
+        // dV: transform input and output tensor descriptors
+        auto vgrad_grid_desc_nblock_nperblock_oblock_operblock =
+            MakeVGradGridDesc_NBlock_NPerBlock_OBlock_OPerBlock(vgrad_grid_desc_n0_o_n1);
+
+        // dK: transform input and output tensor descriptors
+        const auto q_grid_desc_m0_k_m1 =
+            KGradGemmTile_N_K_M::MakeQGridDesc_M0_K_M1(q_grid_desc_k0_m_k1);
+
+        // dK: A matrix blockwise copy
+        auto kgrad_gemm_tile_sgrad_blockwise_copy =
+            typename Gemm1::template ABlockwiseCopy<tensor_operation::element_wise::PassThrough>{
+                tensor_operation::element_wise::PassThrough{}};
+
+        // dK: B matrix blockwise copy
+        auto kgrad_gemm_tile_q_blockwise_copy =
+            typename Gemm1::template BBlockwiseCopy<decltype(q_grid_desc_m0_k_m1)>(
+                q_grid_desc_m0_k_m1,
+                make_multi_index(MPerBlock / B1K1 * (num_gemm0_m_block_outer_loop - 1), 0, 0),
+                b1_element_op,
+                Gemm1::b_block_desc_bk0_n_bk1, // there n actually is k, k is N, so name can be
+                                               // b_block_desc_bn0_k_bn1
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        const auto kgrad_gemm_tile_q_block_next_copy_step =
+            make_multi_index(-2 * MPerBlock / B1K1, 0, 0);
+
+        // dK: blockwise gemm
+        auto kgrad_blockwise_gemm =
+            typename Gemm1::BlockwiseGemm{make_tuple(0, 0, 0, 0)}; // A_origin
+
+        auto kgrad_thread_buf = kgrad_blockwise_gemm.GetCThreadBuffer();
+
+        // dK: transform input and output tensor descriptors
+        auto kgrad_grid_desc_nblock_nperblock_oblock_operblock =
+            MakeKGradGridDesc_NBlock_NPerBlock_OBlock_OPerBlock(kgrad_grid_desc_k0_n_k1);
+
+        //
+        // set up dQ Gemm (type 3 crr)
+        //
+        using Gemm2 = Gemm2<Gemm2Params, decltype(pgrad_blockwise_gemm)>;
+
+        // Gemm2: LDS allocation for A and B: be careful of alignment
+        auto gemm2_a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<GemmDataType*>(p_shared) + SharedMemTrait::a2_block_space_offset,
+            Gemm2::a_block_desc_k0_m_k1.GetElementSpaceSize());
+
+        auto gemm2_b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<GemmDataType*>(p_shared) + SharedMemTrait::b2_block_space_offset,
+            Gemm2::b_block_desc_k0_n_k1.GetElementSpaceSize());
+
+        // // dQ: transform input and output tensor descriptors
+        // const auto vgrad_grid_desc_n0_o0_n1_o1_n2_o2_o3_o4 =
+        //     Gemm2::MakeCGridDesc_N0_O0_N1_O1_N2_O2_O3_O4(vgrad_grid_desc_n_o);
+        // dQ: transform input and output tensor descriptors
+        const auto k_grid_desc_n0_k_n1 =
+            QGradGemmTile_M_K_N::MakeKGridDesc_N0_K_N1(k_grid_desc_k0_n_k1);
+
+        // dQ: A matrix VGPR-to-LDS blockwise copy
+        auto qgrad_gemm_tile_sgrad_thread_copy_vgpr_to_lds =
+            typename Gemm2::template ABlockwiseCopy<tensor_operation::element_wise::PassThrough>{
+                Gemm2::a_block_desc_m0_k0_m1_k1_m2_m3_m4_k2,
+                Gemm2::MakeAThreadOriginOnBlock_M0_K0_M1_K1_M2_M3_M4_K2(),
+                tensor_operation::element_wise::PassThrough{}};
+
+        // dQ: B matrix global-to-LDS blockwise copy
+        auto qgrad_gemm_tile_k_blockwise_copy =
+            typename Gemm2::template BBlockwiseCopy<decltype(k_grid_desc_n0_k_n1)>(
+                k_grid_desc_n0_k_n1,
+                make_multi_index(n_block_data_idx_on_grid / Gemm2Params::B_K1, 0, 0),
+                tensor_operation::element_wise::PassThrough{},
+                Gemm2::b_block_desc_k0_n_k1,
+                make_multi_index(0, 0, 0),
+                tensor_operation::element_wise::PassThrough{});
+
+        // dQ: blockwise gemm
+        auto qgrad_blockwise_gemm = typename Gemm2::BlockwiseGemm{};
+
+        auto qgrad_thread_buf = qgrad_blockwise_gemm.GetCThreadBuffer();
+
+        // dQ: transform output tensor descriptors
+        const auto qgrad_grid_desc_m_k = MakeQGradGridDesc_M_K(q_grid_desc_k0_m_k1);
+        const auto qgrad_grid_desc_m0_o0_m1_o1_m2_o2_o3_o4 =
+            Gemm2::MakeCGridDesc_M0_N0_M1_N1_M2_N2_N3_N4(qgrad_grid_desc_m_k);
+
+        // dQ: C VGPR-to-global copy
+        const auto qgrad_thread_origin_on_grid_m0_o0_m1_o1_m2_o2_o3_o4 =
+            Gemm2::GetCThreadOriginOnBlock_M0_N0_M1_N1_M2_N2_N3_N4() +
+            make_multi_index((num_gemm0_m_block_outer_loop - 1) * Gemm2Params::GemmMRepeat,
+                             I0,
+                             I0,
+                             I0,
+                             I0,
+                             I0,
+                             I0,
+                             I0);
+
+        auto qgrad_thread_copy_vgpr_to_global = typename Gemm2::template CBlockwiseCopy<
+            decltype(qgrad_grid_desc_m0_o0_m1_o1_m2_o2_o3_o4),
+            decltype(scale_rp_dropout)>(qgrad_grid_desc_m0_o0_m1_o1_m2_o2_o3_o4,
+                                        qgrad_thread_origin_on_grid_m0_o0_m1_o1_m2_o2_o3_o4,
+                                        scale_rp_dropout);
+
+        //
+        // Blockwise softmax
+        //
+        // get acc0 8D thread cluster
+        constexpr auto thread_cluster_m0_n0_m1_n1_m2_m3_m4_n2 =
+            s_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths() /
+            s_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+        constexpr auto tm0 = thread_cluster_m0_n0_m1_n1_m2_m3_m4_n2.At(I0);
+        constexpr auto tn0 = thread_cluster_m0_n0_m1_n1_m2_m3_m4_n2.At(I1);
+        constexpr auto tm1 = thread_cluster_m0_n0_m1_n1_m2_m3_m4_n2.At(I2);
+        constexpr auto tn1 = thread_cluster_m0_n0_m1_n1_m2_m3_m4_n2.At(I3);
+        constexpr auto tm2 = thread_cluster_m0_n0_m1_n1_m2_m3_m4_n2.At(I4);
+        constexpr auto tm3 = thread_cluster_m0_n0_m1_n1_m2_m3_m4_n2.At(I5);
+        constexpr auto tm4 = thread_cluster_m0_n0_m1_n1_m2_m3_m4_n2.At(I6);
+        constexpr auto tn2 = thread_cluster_m0_n0_m1_n1_m2_m3_m4_n2.At(I7);
+
+        // get acc0 thread map
+        constexpr auto n0_m_n1_to_m_n_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_unmerge_transform(make_tuple(tn0 * tn1, tn2)),
+                       make_pass_through_transform(I1)),
+            make_tuple(Sequence<1>{}, Sequence<0>{}),
+            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+        constexpr auto threadid_to_n0_m_n1_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(
+                make_merge_transform(make_tuple(tn0 * tn1, tm0 * tm1 * tm2 * tm3 * tm4, tn2))),
+            make_tuple(Sequence<0, 1, 2>{}),
+            make_tuple(Sequence<0>{}));
+        const auto threadid_to_m_n_thread_cluster_adaptor =
+            chain_tensor_adaptors(n0_m_n1_to_m_n_adaptor, threadid_to_n0_m_n1_adaptor);
+
+        // get acc0 2D thread cluster & 2D thread slice
+        constexpr auto thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
+            s_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+        constexpr auto m0 = thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I0);
+        constexpr auto n0 = thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I1);
+        constexpr auto m1 = thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I2);
+        constexpr auto n1 = thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I3);
+        constexpr auto m2 = thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I4);
+        constexpr auto m3 = thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I5);
+        constexpr auto m4 = thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I6);
+        constexpr auto n2 = thread_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I7);
+
+        constexpr auto thread_cluster_desc_m_n = make_naive_tensor_descriptor_packed(
+            make_tuple(tm0 * tm1 * tm2 * tm3 * tm4, tn0 * tn1 * tn2));
+        constexpr auto thread_slice_desc_m_n =
+            make_naive_tensor_descriptor_packed(make_tuple(m0 * m1 * m2 * m3 * m4, n0 * n1 * n2));
+
+        auto blockwise_softmax = BlockwiseSoftmax<BlockSize,
+                                                  FloatGemmAcc,
+                                                  decltype(threadid_to_m_n_thread_cluster_adaptor),
+                                                  decltype(thread_cluster_desc_m_n),
+                                                  decltype(thread_slice_desc_m_n)>{};
+
+        auto blockwise_dropout = BlockwiseDropout<FloatGemmAcc, decltype(thread_slice_desc_m_n)>{
+            p_dropout_in_uint8_t, rp_dropout};
+
+        auto lse_grid_desc_mb_m0_m1_m2_m3_m4 =
+            MakeLSEGridDescriptor_MB_M0_M1_M2_M3_M4<decltype(s_blockwise_gemm)>(lse_grid_desc_m);
+
+        constexpr auto lse_thread_desc_mb_m0_m1_m2_m3_m4 =
+            make_naive_tensor_descriptor_packed(make_tuple(I1, m0, m1, m2, m3, m4));
+
+        auto lse_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatLSE>(
+            lse_thread_desc_mb_m0_m1_m2_m3_m4.GetElementSpaceSize());
+
+        auto acc0_thread_origin = s_blockwise_gemm.CalculateCThreadOriginDataIndex8D(
+            Number<0>{}, Number<0>{}, Number<0>{}, Number<0>{});
+
+        auto lse_thread_copy_global_to_vgpr =
+            ThreadwiseTensorSliceTransfer_v2<FloatLSE,
+                                             FloatLSE,
+                                             decltype(lse_grid_desc_mb_m0_m1_m2_m3_m4),
+                                             decltype(lse_thread_desc_mb_m0_m1_m2_m3_m4),
+                                             Sequence<1, m0, m1, m2, m3, m4>,
+                                             Sequence<0, 1, 2, 3, 4, 5>,
+                                             5,
+                                             1,
+                                             1,
+                                             true /* ResetCoordAfterRun */>{
+                lse_grid_desc_mb_m0_m1_m2_m3_m4,
+                make_multi_index(num_gemm0_m_block_outer_loop - 1, // mblock
+                                 acc0_thread_origin[I0],           // mrepeat
+                                 acc0_thread_origin[I2],           // mwave
+                                 acc0_thread_origin[I4],           // mperxdl
+                                 acc0_thread_origin[I5],
+                                 acc0_thread_origin[I6])};
+
+        //
+        // z vgpr copy to global
+        //
+        // z matrix threadwise desc
+        constexpr auto z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3 =
+            make_naive_tensor_descriptor_packed(make_tuple(I1,   // MBlockId
+                                                           I1,   // NBlockID
+                                                           m0,   // MRepeat
+                                                           n0,   // NRepeat
+                                                           m1,   // MWaveId
+                                                           n1,   // NWaveId
+                                                           m2,   // MGroupNum
+                                                           m3,   // MInputNum
+                                                           m4,   // RegisterNum
+                                                           n2)); // NPerXdl
+
+        StaticBuffer<AddressSpaceEnum::Vgpr,
+                     uint8_t,
+                     z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3.GetElementSpaceSize(),
+                     true>
+            z_tensor_buffer;
+        z_tensor_buffer.Clear();
+
+        auto z_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_z_grid, z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3.GetElementSpaceSize());
+
+        const auto wave_id     = GetGemm0WaveIdx();
+        const auto wave_m_n_id = GetGemm0WaveMNIdx(wave_id[I2]); // I2: 0~63
+
+        auto z_thread_copy_vgpr_to_global = ThreadwiseTensorSliceTransfer_v1r3<
+            uint8_t,
+            ZDataType,
+            decltype(z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3),
+            decltype(z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3),
+            tensor_operation::element_wise::PassThrough,
+            Sequence<I1, // MBlockId
+                     I1, // NBlockID
+                     m0, // MRepeat
+                     n0, // NRepeat
+                     m1, // MWaveId
+                     n1, // NWaveId
+                     m2, // MPerXdl
+                     m3, // NGroupNum
+                     m4, // NInputNum
+                     n2>,
+            Sequence<0, 1, 2, 3, 4, 5, 6, 7, 8, 9>,
+            9, // DstVectorDim
+            1, // DstScalarPerVector
+            InMemoryDataOperationEnum::Set,
+            1, // DstScalarStrideInVector
+            true>{z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                  make_multi_index(num_gemm0_m_block_outer_loop - 1, // MBlockId
+                                   block_work_idx_n,                 // NBlockId
+                                   0,                                // MRepeat
+                                   0,                                // NRepeat
+                                   wave_id[I0],                      // MWaveId
+                                   wave_id[I1],                      // NWaveId
+                                   0,                                // MPerXdl
+                                   wave_m_n_id[I0],                  //
+                                   0,                                //
+                                   wave_m_n_id[I1]),                 // NPerXdl
+                  tensor_operation::element_wise::PassThrough{}};
+
+        //
+        // set up Y dot dY
+        //
+
+        // m0, n0 are m/n repeat per wave
+        // m1, n1 are number of waves
+        constexpr auto p_block_lengths =
+            s_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+        constexpr auto P_M0 = p_block_lengths[I0]; // repeats
+        constexpr auto P_M1 = p_block_lengths[I2]; // waves
+        constexpr auto P_M2 = p_block_lengths[I4]; // xdl
+        constexpr auto P_M3 = p_block_lengths[I5];
+        constexpr auto P_M4 = p_block_lengths[I6];
+
+        constexpr auto y_thread_desc_m0_m1_o0_o1 = make_naive_tensor_descriptor_packed(make_tuple(
+            I1, YDotYGrad_M_O::ThreadSliceLength_M, I1, YDotYGrad_M_O::ThreadSliceLength_O));
+        constexpr auto y_thread_cluster_desc =
+            make_cluster_descriptor(Sequence<I1,
+                                             YDotYGrad_M_O::ThreadClusterLength_M,
+                                             I1,
+                                             YDotYGrad_M_O::ThreadClusterLength_O>{},
+                                    Sequence<0, 1, 2, 3>{});
+        const auto y_thread_cluster_idx =
+            y_thread_cluster_desc.CalculateBottomIndex(make_multi_index(get_thread_local_1d_id()));
+
+        const auto y_thread_data_on_block_idx =
+            y_thread_cluster_idx * y_thread_desc_m0_m1_o0_o1.GetLengths();
+        const auto y_thread_data_on_grid_idx =
+            make_multi_index(num_gemm0_m_block_outer_loop - 1, I0, I0, I0) +
+            y_thread_data_on_block_idx;
+
+        // performs double duty for both y and ygrad
+        auto yygrad_threadwise_copy = ThreadwiseTensorSliceTransfer_v2<
+            InputDataType,
+            FloatGemmAcc,
+            YGridDescriptor_MBlock_MPerBlock_OBlock_OPerBlock,
+            decltype(y_thread_desc_m0_m1_o0_o1),
+            decltype(y_thread_desc_m0_m1_o0_o1.GetLengths()),
+            Sequence<0, 1, 2, 3>,
+            3,                                 // SrcVectorDim
+            YDotYGrad_M_O::SrcScalarPerVector, // SrcScalarPerVector
+            1,                                 // SrcScalarStrideInVector
+            true /* ResetCoordAfterRun */,
+            false /* InvalidElementAsNaN */>(y_grid_desc_mblock_mperblock_oblock_operblock,
+                                             y_thread_data_on_grid_idx);
+
+        auto y_thread_buf                 = typename YDotYGrad_M_O::SrcBufType{};
+        auto ygrad_thread_buf             = typename YDotYGrad_M_O::SrcBufType{};
+        auto y_dot_ygrad_thread_accum_buf = typename YDotYGrad_M_O::DstBufType{};
+        auto y_dot_ygrad_block_accum_buf  = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<FloatGemmAcc*>(p_shared), MPerBlock);
+
+        constexpr auto y_dot_ygrad_block_desc_mb_m0_m1_m2_m3_m4 =
+            make_naive_tensor_descriptor_packed(make_tuple(I1, P_M0, P_M1, P_M2, P_M3, P_M4));
+        constexpr auto y_dot_ygrad_thread_desc_mb_m0_m1_m2_m3_m4 =
+            lse_thread_desc_mb_m0_m1_m2_m3_m4; // reuse LSE thread descriptor because
+                                               // per-thread LSE data and y_dot_ygrad is
+                                               // tiled the same way
+
+        auto y_dot_ygrad_thread_copy_lds_to_vgpr =
+            ThreadwiseTensorSliceTransfer_v2<FloatGemmAcc,
+                                             FloatGemmAcc,
+                                             decltype(y_dot_ygrad_block_desc_mb_m0_m1_m2_m3_m4),
+                                             decltype(y_dot_ygrad_thread_desc_mb_m0_m1_m2_m3_m4),
+                                             Sequence<1, m0, m1, m2, m3, m4>,
+                                             Sequence<0, 1, 2, 3, 4, 5>,
+                                             5,
+                                             1,
+                                             1,
+                                             true /* ResetCoordAfterRun */>{
+                y_dot_ygrad_block_desc_mb_m0_m1_m2_m3_m4,
+                make_multi_index(I0,                     // mblock
+                                 acc0_thread_origin[I0], // mrepeat
+                                 acc0_thread_origin[I2], // mwave
+                                 acc0_thread_origin[I4], // mperxdl
+                                 acc0_thread_origin[I5],
+                                 acc0_thread_origin[I6])};
+
+        auto y_dot_ygrad_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatGemmAcc>(
+            y_dot_ygrad_thread_desc_mb_m0_m1_m2_m3_m4.GetElementSpaceSize());
+
+        // gemm0 M loop
+        index_t gemm0_m_block_outer_index = num_gemm0_m_block_outer_loop - 1;
+
+        // D0
+        auto d0_block_copy_global_to_lds = typename D0Operator::D0BlockwiseCopyGlobalToLds(
+            d0_grid_desc_m0_n0_m1_m2_n1_m3,
+            make_multi_index(gemm0_m_block_outer_index, block_work_idx_n, 0, 0, 0, 0),
+            tensor_operation::element_wise::PassThrough{},
+            D0Operator::d0_block_dst_desc_m0_n0_m1_m2_n1_m3,
+            make_multi_index(0, 0, 0, 0, 0, 0),
+            tensor_operation::element_wise::PassThrough{});
+
+        auto d0_thread_copy_lds_to_vgpr = typename D0Operator::D0ThreadwiseCopyLdsToVgpr(
+            make_tuple(wave_id[I1], wave_m_n_id[I1], 0, wave_m_n_id[I0], 0));
+
+        auto& d0grad_grid_desc_m0_n0_m1_m2_n1_m3 = d0_grid_desc_m0_n0_m1_m2_n1_m3;
+        auto d0grad_thread_copy_vgpr_to_lds = typename D0Operator::D0GradThreadwiseCopyVgprToLds(
+            D0Operator::d0grad_block_dst_desc_n0_n1_m0_m1_m2,
+            make_tuple(wave_id[I1], wave_m_n_id[I1], 0, wave_m_n_id[I0], 0),
+            tensor_operation::element_wise::Scale{rp_dropout});
+
+        auto d0grad_block_copy_lds_to_global = typename D0Operator::D0GradBlockwiseCopyLdsToGlobal(
+            D0Operator::d0grad_block_src_desc_m0_n0_m1_m2_n1_m3,
+            make_multi_index(0, 0, 0, 0, 0, 0),
+            tensor_operation::element_wise::PassThrough{},
+            d0grad_grid_desc_m0_n0_m1_m2_n1_m3,
+            make_multi_index(gemm0_m_block_outer_index, block_work_idx_n, 0, 0, 0, 0),
+            tensor_operation::element_wise::PassThrough{});
+
+        if constexpr(Deterministic)
+        {
+            block_sync_lds();
+        }
+
+        // Initialize dK&dV
+        kgrad_thread_buf.Clear();
+        vgrad_thread_buf.Clear();
+
+        do
+        {
+            auto m_block_data_idx_on_grid =
+                __builtin_amdgcn_readfirstlane(gemm0_m_block_outer_index * MPerBlock);
+            if(c0_matrix_mask.IsTileSkippable(
+                   m_block_data_idx_on_grid, n_block_data_idx_on_grid, MPerBlock, NPerBlock))
+            {
+                continue;
+            }
+
+            //
+            // calculate Y dot dY
+            //
+
+            // clear accum buffers
+            y_dot_ygrad_thread_accum_buf.Clear();
+            y_dot_ygrad_block_accum_buf.Clear();
+
+            yygrad_threadwise_copy.Run(y_grid_desc_mblock_mperblock_oblock_operblock,
+                                       y_grid_buf,
+                                       y_thread_desc_m0_m1_o0_o1,
+                                       make_tuple(I0, I0, I0, I0),
+                                       y_thread_buf);
+            yygrad_threadwise_copy.Run(y_grid_desc_mblock_mperblock_oblock_operblock,
+                                       ygrad_grid_buf,
+                                       y_thread_desc_m0_m1_o0_o1,
+                                       make_tuple(I0, I0, I0, I0),
+                                       ygrad_thread_buf);
+
+            static_for<0, YDotYGrad_M_O::ThreadSliceLength_M, 1>{}([&](auto iM) {
+                static_for<0, YDotYGrad_M_O::ThreadSliceLength_O, 1>{}([&](auto iO) {
+                    constexpr auto offset =
+                        y_thread_desc_m0_m1_o0_o1.CalculateOffset(make_multi_index(I0, iM, I0, iO));
+                    y_dot_ygrad_thread_accum_buf(iM) +=
+                        y_thread_buf[Number<offset>{}] * ygrad_thread_buf[Number<offset>{}];
+                });
+            });
+
+            // blockwise reduction using atomic_add
+            block_sync_lds();
+            static_for<0, YDotYGrad_M_O::ThreadSliceLength_M, 1>{}([&](auto iM) {
+                const auto idx_on_block = y_thread_data_on_block_idx[I1] + iM;
+                y_dot_ygrad_block_accum_buf.AtomicAdd(idx_on_block,
+                                                      true,
+                                                      y_dot_ygrad_thread_accum_buf[iM] *
+                                                          p_dropout); // p_dropoutD1
+            });
+            block_sync_lds();
+
+            // distribute y_dot_ygrad to threads;
+            // LDS accum buffer can be safely reused after barrier
+            y_dot_ygrad_thread_copy_lds_to_vgpr.Run(y_dot_ygrad_block_desc_mb_m0_m1_m2_m3_m4,
+                                                    y_dot_ygrad_block_accum_buf,
+                                                    y_dot_ygrad_thread_desc_mb_m0_m1_m2_m3_m4,
+                                                    make_tuple(I0, I0, I0, I0, I0, I0),
+                                                    y_dot_ygrad_thread_buf);
+
+            block_sync_lds();
+
+            lse_thread_copy_global_to_vgpr.Run(lse_grid_desc_mb_m0_m1_m2_m3_m4,
+                                               lse_grid_buf,
+                                               lse_thread_desc_mb_m0_m1_m2_m3_m4,
+                                               make_tuple(I0, I0, I0, I0, I0, I0),
+                                               lse_thread_buf);
+
+            // S = Q * K^T
+            gemm0_gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(
+                q_grid_desc_k0_m_k1,
+                Gemm0::a_block_desc_ak0_m_ak1,
+                s_gemm_tile_q_blockwise_copy,
+                q_grid_buf,
+                gemm0_a_block_buf,
+                Gemm0::a_block_slice_copy_step,
+                k_grid_desc_k0_n_k1,
+                Gemm0::b_block_desc_bk0_n_bk1,
+                s_gemm_tile_k_blockwise_copy,
+                k_grid_buf,
+                gemm0_b_block_buf,
+                Gemm0::b_block_slice_copy_step,
+                s_blockwise_gemm,
+                s_slash_p_thread_buf,
+                num_k_block_main_loop);
+
+            // 8d thread_desc in thread scope
+            constexpr auto c_thread_lengths =
+                s_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+            // 8d block_desc in block scope
+            constexpr auto c_block_lengths =
+                s_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2().GetLengths();
+
+            constexpr auto M0 = c_block_lengths[I0];
+            constexpr auto N0 = c_block_lengths[I1];
+            constexpr auto M1 = c_block_lengths[I2];
+            constexpr auto N1 = c_block_lengths[I3];
+            constexpr auto M2 = c_block_lengths[I4];
+            constexpr auto M3 = c_block_lengths[I5];
+            constexpr auto M4 = c_block_lengths[I6];
+            constexpr auto N2 = c_block_lengths[I7];
+
+            // works like multi-dimension static_for (static_ford), but provides both the linear
+            // index as well as n-d index
+            using Acc0TileIterator = SpaceFillingCurve<
+                decltype(c_thread_lengths),
+                typename arithmetic_sequence_gen<0, c_thread_lengths.Size(), 1>::type,
+                typename uniform_sequence_gen<c_thread_lengths.Size(), 1>::type,
+                false>; // SnakeCurved
+
+            constexpr auto block_idx_to_m_n_adaptor = make_single_stage_tensor_adaptor(
+                make_tuple(make_unmerge_transform(make_tuple(M0, M1, M2, M3, M4)),
+                           make_unmerge_transform(make_tuple(N0, N1, N2))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2, 4, 5, 6>{}, Sequence<1, 3, 7>{}));
+
+            // do MNK padding or upper triangular masking
+            if constexpr(MaskOutUpperTriangle || PadN)
+            {
+
+                static_for<0, Acc0TileIterator::GetNumOfAccess(), 1>{}([&](auto i) {
+                    auto acc0_thread_idx = Acc0TileIterator::GetIndex(i) + acc0_thread_origin;
+                    auto m_local =
+                        block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I0];
+                    auto n_local =
+                        block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I1];
+                    auto m_global    = m_local + m_block_data_idx_on_grid;
+                    auto n_global    = n_local + n_block_data_idx_on_grid;
+                    bool masked_flag = c0_matrix_mask.IsMaskedElement(m_global, n_global);
+                    s_element_op(s_slash_p_thread_buf(i),
+                                 masked_flag ? -ck::NumericLimits<float>::Infinity()
+                                             : s_slash_p_thread_buf[i]);
+                });
+            }
+            else
+            {
+                static_for<0, s_slash_p_thread_buf.Size(), 1>{}([&](auto i) {
+                    s_element_op(s_slash_p_thread_buf(i), s_slash_p_thread_buf[i]);
+                });
+            }
+
+            block_sync_lds(); // wait for lds read in gemm0 blockwise gemm
+
+            // add bias
+            if constexpr(!is_same<D0DataType, void>::value)
+            {
+                if(p_d0_grid != nullptr)
+                {
+                    static constexpr auto& c_thread_desc = s_blockwise_gemm.GetCThreadDesc();
+
+                    const auto d0_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+                        p_d0_grid, d0_grid_desc_m0_n0_m1_m2_n1_m3.GetElementSpaceSize());
+
+                    auto d0_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                        static_cast<D0DataType*>(p_shared) + SharedMemTrait::d0_block_space_offset,
+                        D0Operator::d0_block_dst_desc_m0_n0_m1_m2_n1_m3.GetElementSpaceSize());
+
+                    auto d0_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, D0DataType>(
+                        D0Operator::d0_thread_desc_.GetElementSpaceSize());
+
+                    static_for<0, D0M0, 1>{}([&](auto mr) {
+                        // load data to lds
+                        d0_block_copy_global_to_lds.RunRead(d0_grid_desc_m0_n0_m1_m2_n1_m3,
+                                                            d0_grid_buf);
+
+                        d0_block_copy_global_to_lds.MoveSrcSliceWindow(
+                            d0_grid_desc_m0_n0_m1_m2_n1_m3, make_multi_index(0, 0, 1, 0, 0, 0));
+
+                        d0_block_copy_global_to_lds.RunWrite(
+                            D0Operator::d0_block_dst_desc_m0_n0_m1_m2_n1_m3, d0_block_buf);
+                        block_sync_lds();
+                        // read data form lds
+                        d0_thread_copy_lds_to_vgpr.Run(D0Operator::d0_block_src_desc_n0_n1_m0_m1_m2,
+                                                       make_tuple(I0, I0, I0, I0, I0),
+                                                       d0_block_buf,
+                                                       D0Operator::d0_thread_desc_,
+                                                       make_tuple(I0, I0, I0, I0, I0),
+                                                       d0_thread_buf);
+
+                        // bias add
+                        static_for<0, d0_thread_buf.Size(), 1>{}([&](auto i) {
+                            constexpr index_t c_offset =
+                                c_thread_desc.CalculateOffset(make_tuple(mr, I0, i));
+
+                            s_slash_p_thread_buf(Number<c_offset>{}) +=
+                                ck::type_convert<FloatGemmAcc>(d0_thread_buf[i]);
+                        });
+                    });
+
+                    d0_block_copy_global_to_lds.MoveSrcSliceWindow(
+                        d0_grid_desc_m0_n0_m1_m2_n1_m3,
+                        make_multi_index(-1, 0, -D0M0.value, 0, 0, 0));
+                }
+            }
+
+            // P_i: = softmax(scalar * S_i:)
+            // scaling is already performed in the preceding statements with s_element_op
+            blockwise_softmax.RunWithPreCalcStats(s_slash_p_thread_buf, lse_thread_buf);
+
+            // save z to global
+            if constexpr(IsDropout)
+            {
+                if(p_z_grid)
+                {
+
+                    auto acc0_thread_idx = Acc0TileIterator::GetIndex(I0) + acc0_thread_origin;
+                    auto m_local =
+                        block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I0];
+                    auto n_local =
+                        block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I1];
+                    auto m_global = m_local + m_block_data_idx_on_grid;
+                    auto n_global = n_local + n_block_data_idx_on_grid;
+
+                    auto global_tile_id = z_random_matrix_offset +
+                                          (m_global / DropoutTile) * DropoutTile * raw_n_padded +
+                                          (n_global / DropoutTile) * DropoutTile;
+
+                    auto global_elem_id = global_tile_id + (wave_m_n_id[I0] * M4) +
+                                          (n_global % DropoutTile) * raw_n_padded;
+
+                    blockwise_dropout
+                        .template ApplyDropoutAttnBwdSaveZ<decltype(s_slash_p_thread_buf),
+                                                           decltype(z_tensor_buffer),
+                                                           decltype(DropoutTile),
+                                                           true>(s_slash_p_thread_buf,
+                                                                 ph,
+                                                                 global_elem_id,
+                                                                 z_tensor_buffer,
+                                                                 raw_n_padded);
+
+                    z_thread_copy_vgpr_to_global.Run(
+                        z_thread_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                        make_tuple(I0, I0, I0, I0, I0, I0, I0, I0, I0, I0),
+                        z_tensor_buffer,
+                        z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                        z_grid_buf);
+                }
+                else
+                {
+                    ignore = z_grid_buf;
+
+                    auto acc0_thread_idx = Acc0TileIterator::GetIndex(I0) + acc0_thread_origin;
+                    auto m_local =
+                        block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I0];
+                    auto n_local =
+                        block_idx_to_m_n_adaptor.CalculateBottomIndex(acc0_thread_idx)[I1];
+                    auto m_global = m_local + m_block_data_idx_on_grid;
+                    auto n_global = n_local + n_block_data_idx_on_grid;
+
+                    auto global_tile_id = z_random_matrix_offset +
+                                          (m_global / DropoutTile) * DropoutTile * raw_n_padded +
+                                          (n_global / DropoutTile) * DropoutTile;
+
+                    auto global_elem_id = global_tile_id + (wave_m_n_id[I0] * M4) +
+                                          (n_global % DropoutTile) * raw_n_padded;
+
+                    // P_dropped
+                    blockwise_dropout.template ApplyDropoutAttnBwd<decltype(s_slash_p_thread_buf),
+                                                                   decltype(DropoutTile),
+                                                                   true>(
+                        s_slash_p_thread_buf, ph, global_elem_id, raw_n_padded);
+                }
+            }
+            block_sync_lds(); // wait for gemm1 LDS read
+
+            // gemm dV
+            // dV = P_drop^T * dY
+            {
+                // TODO: explore using dynamic buffer for a1 thread buffer
+                // For a1_blockwise_copy, the goal is to satisfy pipeline requirements RunRead(),
+                // RunWrite(), and MoveSliceWindow(). But it is impossible to implement given that
+                // the A1 source buffer is static buffer holding the output of first GEMM and
+                // requires constexpr offset by design. Therefore, we pass tensor coordinate offset
+                // explicitly in Run() below.
+
+                // preload data into LDS
+                vgrad_gemm_tile_ygrad_blockwise_copy.RunRead(ygrad_grid_desc_m0_o_m1,
+                                                             ygrad_grid_buf);
+
+                vgrad_gemm_tile_ygrad_blockwise_copy.MoveSrcSliceWindow(
+                    ygrad_grid_desc_m0_o_m1, Gemm1::b_block_slice_copy_step);
+
+                block_sync_lds(); // wait for previous LDS read
+
+                vgrad_gemm_tile_ygrad_blockwise_copy.RunWrite(Gemm1::b_block_desc_bk0_n_bk1,
+                                                              gemm1_b_block_buf);
+
+                // main body
+                if constexpr(num_gemm1_k_block_inner_loop > 1)
+                {
+                    static_for<0, num_gemm1_k_block_inner_loop - 1, 1>{}([&](auto i) {
+                        vgrad_gemm_tile_p_blockwise_copy.Run(Gemm1::a_src_thread_desc_k0_m_k1,
+                                                             Gemm1::a_block_slice_copy_step * i,
+                                                             s_slash_p_thread_buf,
+                                                             Gemm1::a_thread_desc_k0_m_k1,
+                                                             make_tuple(I0, I0, I0),
+                                                             gemm1_a_thread_buf);
+                        vgrad_gemm_tile_ygrad_blockwise_copy.RunRead(ygrad_grid_desc_m0_o_m1,
+                                                                     ygrad_grid_buf);
+
+                        block_sync_lds();
+
+                        vgrad_blockwise_gemm.Run(
+                            gemm1_a_thread_buf, gemm1_b_block_buf, vgrad_thread_buf);
+
+                        block_sync_lds();
+
+                        vgrad_gemm_tile_ygrad_blockwise_copy.MoveSrcSliceWindow(
+                            ygrad_grid_desc_m0_o_m1, Gemm1::b_block_slice_copy_step);
+
+                        vgrad_gemm_tile_ygrad_blockwise_copy.RunWrite(Gemm1::b_block_desc_bk0_n_bk1,
+                                                                      gemm1_b_block_buf);
+                    });
+                }
+                // tail
+                {
+                    vgrad_gemm_tile_p_blockwise_copy.Run(
+                        Gemm1::a_src_thread_desc_k0_m_k1,
+                        Gemm1::a_block_slice_copy_step * Number<num_gemm1_k_block_inner_loop - 1>{},
+                        s_slash_p_thread_buf,
+                        Gemm1::a_thread_desc_k0_m_k1,
+                        make_tuple(I0, I0, I0),
+                        gemm1_a_thread_buf);
+
+                    block_sync_lds();
+
+                    vgrad_blockwise_gemm.Run(
+                        gemm1_a_thread_buf, gemm1_b_block_buf, vgrad_thread_buf);
+                }
+            } // end gemm dV
+
+            // gemm dP
+            block_sync_lds();
+            // dP = dY * V^T
+            // assume size K == size O so HasMainKBlockLoop is the same
+            gemm0_gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(
+                ygrad_grid_desc_o0_m_o1,
+                Gemm0::a_block_desc_ak0_m_ak1, // reuse
+                pgrad_gemm_tile_ygrad_blockwise_copy,
+                ygrad_grid_buf,
+                gemm0_a_block_buf,              // reuse
+                Gemm0::a_block_slice_copy_step, // reuse
+                v_grid_desc_o0_n_o1,
+                Gemm0::b_block_desc_bk0_n_bk1, // reuse
+                pgrad_gemm_tile_v_blockwise_copy,
+                v_grid_buf,
+                gemm0_b_block_buf,              // reuse
+                Gemm0::b_block_slice_copy_step, // reuse
+                pgrad_blockwise_gemm,
+                pgrad_thread_buf,
+                num_o_block_main_loop);
+
+            // dS = P * (dP - Y_dot_dY)
+            auto& sgrad_thread_buf = pgrad_thread_buf;
+            constexpr auto pgrad_thread_tile_iterator =
+                pgrad_blockwise_gemm.MakeCThreadTileIterator();
+            constexpr auto pgrad_thread_idx_to_m_n_adaptor =
+                pgrad_blockwise_gemm.MakeCThreadIndexAdaptor8DTo2D();
+            static_for<0, pgrad_thread_tile_iterator.GetNumOfAccess(), 1>{}([&](auto i) {
+                constexpr auto pgrad_thread_idx = pgrad_thread_tile_iterator.GetIndex(i);
+                constexpr auto m =
+                    pgrad_thread_idx_to_m_n_adaptor.CalculateBottomIndex(pgrad_thread_idx)[I0];
+                // dS and P has same thread buf layout
+                bool undropped_flag = s_slash_p_thread_buf[i] >= 0;
+                sgrad_thread_buf(i) =
+                    s_slash_p_thread_buf[i] *
+                    (undropped_flag ? (pgrad_thread_buf[i] - y_dot_ygrad_thread_buf[Number<m>{}])
+                                    : y_dot_ygrad_thread_buf[Number<m>{}]);
+            });
+
+            // output bias grad
+            if constexpr(!is_same<D0DataType, void>::value)
+            {
+                if(p_d0grad_grid != nullptr)
+                {
+                    auto d0grad_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+                        p_d0grad_grid, d0grad_grid_desc_m0_n0_m1_m2_n1_m3.GetElementSpaceSize());
+
+                    auto d0grad_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                        static_cast<D0DataType*>(p_shared) + SharedMemTrait::d0_block_space_offset,
+                        D0Operator::d0grad_block_src_desc_m0_n0_m1_m2_n1_m3.GetElementSpaceSize());
+
+                    static_for<0, D0M0, 1>{}([&](auto mr) {
+                        d0grad_thread_copy_vgpr_to_lds.Run(
+                            D0Operator::d0_thread_desc_,
+                            make_tuple(mr, I0, I0, I0, I0),
+                            sgrad_thread_buf,
+                            D0Operator::d0grad_block_dst_desc_n0_n1_m0_m1_m2,
+                            d0grad_block_buf);
+
+                        block_sync_lds();
+
+                        // write data from lds to global
+                        d0grad_block_copy_lds_to_global.Run(
+                            D0Operator::d0grad_block_src_desc_m0_n0_m1_m2_n1_m3,
+                            d0grad_block_buf,
+                            d0grad_grid_desc_m0_n0_m1_m2_n1_m3,
+                            d0grad_grid_buf,
+                            I0);
+
+                        d0grad_block_copy_lds_to_global.MoveDstSliceWindow(
+                            d0grad_grid_desc_m0_n0_m1_m2_n1_m3, make_multi_index(0, 0, 1, 0, 0, 0));
+                    });
+
+                    d0grad_block_copy_lds_to_global.MoveDstSliceWindow(
+                        d0grad_grid_desc_m0_n0_m1_m2_n1_m3,
+                        make_multi_index(-1, 0, -D0M0.value, 0, 0, 0));
+                }
+            }
+
+            SubThreadBlock<BlockSize> gemm2_a_copy_subgroup(s_blockwise_gemm.GetWaveIdx()[I0],
+                                                            s_blockwise_gemm.GetWaveIdx()[I1]);
+
+            constexpr index_t num_gemm2_loop = NPerBlock / Gemm2Params::Sum_K;
+            static_assert(Gemm2::ASrcBlockSliceWindowIterator::GetNumOfAccess() == num_gemm2_loop,
+                          "");
+
+            // TODO: tune gemm2 pipeline
+            // gemm dQ
+            // dQ = scalar * dS * K
+            qgrad_thread_buf.Clear();
+            static_for<0, num_gemm2_loop, 1>{}([&](auto gemm2_loop_idx) { // gemm dQ
+                // load QGrad Gemm B
+                qgrad_gemm_tile_k_blockwise_copy.RunRead(k_grid_desc_n0_k_n1, k_grid_buf);
+
+                // load QGrad Gemm A
+                const auto sgrad_slice_idx =
+                    Gemm2::ASrcBlockSliceWindowIterator::GetIndexTupleOfNumber(gemm2_loop_idx);
+                constexpr auto mwave_range = make_tuple(
+                    sgrad_slice_idx[I2],
+                    sgrad_slice_idx[I2] + Gemm2Params::ABlockSliceLengths_M0_K0_M1_K1::At(I2));
+                constexpr auto nwave_range = make_tuple(
+                    sgrad_slice_idx[I3],
+                    sgrad_slice_idx[I3] + Gemm2Params::ABlockSliceLengths_M0_K0_M1_K1::At(I3));
+
+                block_sync_lds(); // sync before write
+                if(gemm2_a_copy_subgroup.IsBelong(mwave_range, nwave_range))
+                {
+                    qgrad_gemm_tile_sgrad_thread_copy_vgpr_to_lds.Run(
+                        Gemm2::a_src_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                        make_tuple(
+                            sgrad_slice_idx[I0], sgrad_slice_idx[I1], I0, I0, I0, I0, I0, I0),
+                        sgrad_thread_buf,
+                        Gemm2::a_block_desc_m0_k0_m1_k1_m2_m3_m4_k2,
+                        gemm2_a_block_buf);
+                }
+
+                // k slice window is moved with MoveSrcSliceWindow() since it is dynamic buffer
+                // sgrad slice window is moved by loop index
+                qgrad_gemm_tile_k_blockwise_copy.MoveSrcSliceWindow(k_grid_desc_n0_k_n1,
+                                                                    Gemm2::b_block_slice_copy_step);
+
+                qgrad_gemm_tile_k_blockwise_copy.RunWrite(Gemm2::b_block_desc_k0_n_k1,
+                                                          gemm2_b_block_buf);
+
+                block_sync_lds(); // sync before read
+                qgrad_blockwise_gemm.Run(gemm2_a_block_buf, gemm2_b_block_buf, qgrad_thread_buf);
+            }); // end gemm dQ
+            // atomic_add dQ
+            qgrad_thread_copy_vgpr_to_global.Run(Gemm2::c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                                                 make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
+                                                 qgrad_thread_buf,
+                                                 qgrad_grid_desc_m0_o0_m1_o1_m2_o2_o3_o4,
+                                                 qgrad_grid_buf);
+
+            // gemm dK
+            // dK = scalar * dS^T * Q
+            {
+                // TODO: explore using dynamic buffer for a1 thread buffer
+                // For a1_blockwise_copy, the goal is to satisfy pipeline requirements RunRead(),
+                // RunWrite(), and MoveSliceWindow(). But it is impossible to implement given that
+                // the A1 source buffer is static buffer holding the output of first GEMM and
+                // requires constexpr offset by design. Therefore, we pass tensor coordinate offset
+                // explicitly in Run() below.
+
+                // preload data into LDS
+                kgrad_gemm_tile_q_blockwise_copy.RunRead(q_grid_desc_m0_k_m1, q_grid_buf);
+
+                kgrad_gemm_tile_q_blockwise_copy.MoveSrcSliceWindow(q_grid_desc_m0_k_m1,
+                                                                    Gemm1::b_block_slice_copy_step);
+
+                block_sync_lds(); // wait for previous LDS read
+
+                kgrad_gemm_tile_q_blockwise_copy.RunWrite(Gemm1::b_block_desc_bk0_n_bk1,
+                                                          gemm1_b_block_buf);
+
+                // main body
+                if constexpr(num_gemm1_k_block_inner_loop > 1)
+                {
+                    static_for<0, num_gemm1_k_block_inner_loop - 1, 1>{}([&](auto i) {
+                        kgrad_gemm_tile_sgrad_blockwise_copy.Run(Gemm1::a_src_thread_desc_k0_m_k1,
+                                                                 Gemm1::a_block_slice_copy_step * i,
+                                                                 sgrad_thread_buf,
+                                                                 Gemm1::a_thread_desc_k0_m_k1,
+                                                                 make_tuple(I0, I0, I0),
+                                                                 gemm1_a_thread_buf);
+                        kgrad_gemm_tile_q_blockwise_copy.RunRead(q_grid_desc_m0_k_m1, q_grid_buf);
+
+                        block_sync_lds();
+
+                        kgrad_blockwise_gemm.Run(
+                            gemm1_a_thread_buf, gemm1_b_block_buf, kgrad_thread_buf);
+
+                        block_sync_lds();
+
+                        kgrad_gemm_tile_q_blockwise_copy.MoveSrcSliceWindow(
+                            q_grid_desc_m0_k_m1, Gemm1::b_block_slice_copy_step);
+
+                        kgrad_gemm_tile_q_blockwise_copy.RunWrite(Gemm1::b_block_desc_bk0_n_bk1,
+                                                                  gemm1_b_block_buf);
+                    });
+                }
+                // tail
+                {
+                    kgrad_gemm_tile_sgrad_blockwise_copy.Run(
+                        Gemm1::a_src_thread_desc_k0_m_k1,
+                        Gemm1::a_block_slice_copy_step * Number<num_gemm1_k_block_inner_loop - 1>{},
+                        sgrad_thread_buf,
+                        Gemm1::a_thread_desc_k0_m_k1,
+                        make_tuple(I0, I0, I0),
+                        gemm1_a_thread_buf);
+
+                    block_sync_lds();
+
+                    kgrad_blockwise_gemm.Run(
+                        gemm1_a_thread_buf, gemm1_b_block_buf, kgrad_thread_buf);
+                }
+            } // end gemm dK
+
+            // move slice window
+            s_gemm_tile_q_blockwise_copy.MoveSrcSliceWindow(
+                q_grid_desc_k0_m_k1,
+                s_gemm_tile_q_block_reset_copy_step); // rewind K and step M
+            s_gemm_tile_k_blockwise_copy.MoveSrcSliceWindow(
+                k_grid_desc_k0_n_k1,
+                s_gemm_tile_k_block_reset_copy_step); // rewind K
+            pgrad_gemm_tile_ygrad_blockwise_copy.MoveSrcSliceWindow(
+                ygrad_grid_desc_o0_m_o1,
+                pgrad_gemm_tile_ygrad_block_reset_copy_step); // rewind O and step M
+            pgrad_gemm_tile_v_blockwise_copy.MoveSrcSliceWindow(
+                v_grid_desc_o0_n_o1,
+                pgrad_gemm_tile_v_block_reset_copy_step); // rewind O
+            qgrad_gemm_tile_k_blockwise_copy.MoveSrcSliceWindow(
+                k_grid_desc_n0_k_n1,
+                Gemm2::b_block_reset_copy_step); // rewind N
+            kgrad_gemm_tile_q_blockwise_copy.MoveSrcSliceWindow(
+                q_grid_desc_m0_k_m1, kgrad_gemm_tile_q_block_next_copy_step); // step M
+            vgrad_gemm_tile_ygrad_blockwise_copy.MoveSrcSliceWindow(
+                ygrad_grid_desc_m0_o_m1, vgrad_gemm_tile_ygrad_block_next_copy_step); // step M
+            qgrad_thread_copy_vgpr_to_global.MoveDstSliceWindow(
+                qgrad_grid_desc_m0_o0_m1_o1_m2_o2_o3_o4, Gemm2::c_block_slice_copy_step); // step M
+            lse_thread_copy_global_to_vgpr.MoveSrcSliceWindow(lse_grid_desc_mb_m0_m1_m2_m3_m4,
+                                                              make_multi_index(-1, 0, 0, 0, 0, 0));
+            yygrad_threadwise_copy.MoveSrcSliceWindow(y_grid_desc_mblock_mperblock_oblock_operblock,
+                                                      make_multi_index(-1, 0, 0, 0));
+
+            z_thread_copy_vgpr_to_global.MoveDstSliceWindow(
+                z_grid_desc_m0_n0_m1_n1_m2_n2_m3_m4_m5_n3,
+                make_multi_index(-1, 0, 0, 0, 0, 0, 0, 0, 0, 0));
+
+        } while(0 < gemm0_m_block_outer_index--); // end j loop
+
+        // shuffle dK&dV and write
+        {
+            static_assert(NXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
+                              Gemm1NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
+                          "wrong!");
+
+            constexpr index_t MWave = Gemm0NWaves;
+            constexpr index_t NWave = Gemm0MWaves;
+
+            // TODO: hacky, fix it!
+            // thread desc same with kgrad_blockwise_gemm
+            constexpr auto c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4 =
+                vgrad_blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4();
+
+            // TODO: hacky, fix it!
+            // c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp is only used to get lengths
+            // block desc same with kgrad_blockwise_gemm
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp =
+                vgrad_blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4();
+
+            constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I0);
+            constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I1);
+            constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I2);
+            constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I3);
+            constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I4);
+            constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I5);
+            constexpr auto N3 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I6);
+            constexpr auto N4 = c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4_tmp.GetLength(I7);
+
+            constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+                GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+            auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                static_cast<FloatCShuffle*>(p_shared),
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4 = transform_tensor_descriptor(
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                make_tuple(
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
+                        M1,                                      // M1 = MWave
+                        M2)),                                    // M2 = MPerXdl
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
+                        N1,                                      // N1 = NWave
+                        N2,                                      // N2 * N3 * N4 = NPerXdl
+                        N3,
+                        N4))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(
+                    Sequence<>{}, Sequence<0, 2, 4>{}, Sequence<>{}, Sequence<1, 3, 5, 6, 7>{}));
+
+            // calculate origin of thread output tensor on global memory
+            //     blockwise GEMM c matrix starting index
+            // index same with kgrad_blockwise_gemm
+            const auto c_thread_mtx_on_block =
+                vgrad_blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
+
+            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
+            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
+
+            const auto m_thread_data_on_block_to_m0_m1_m2_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2))),
+                    make_tuple(Sequence<0, 1, 2>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto m_thread_data_on_block_idx =
+                m_thread_data_on_block_to_m0_m1_m2_adaptor.CalculateBottomIndex(
+                    make_multi_index(m_thread_data_on_block));
+
+            const auto n_thread_data_on_block_to_n0_n1_n2_n3_n4_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2, N3, N4))),
+                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto n_thread_data_on_block_idx =
+                n_thread_data_on_block_to_n0_n1_n2_n3_n4_adaptor.CalculateBottomIndex(
+                    make_multi_index(n_thread_data_on_block));
+
+            // shuffle: threadwise copy C from VGPR to LDS
+            auto vgrad_thread_copy_vgpr_to_lds =
+                ThreadwiseTensorSliceTransfer_v1r3<FloatGemmAcc,
+                                                   FloatCShuffle,
+                                                   decltype(c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4),
+                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4),
+                                                   SElementwiseOperation,
+                                                   Sequence<CShuffleMXdlPerWavePerShuffle,
+                                                            CShuffleNXdlPerWavePerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            I1,
+                                                            N2,
+                                                            I1,
+                                                            N4>,
+                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                                   7,
+                                                   1,
+                                                   InMemoryDataOperationEnum::Set,
+                                                   1,
+                                                   true>{
+                    c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                    make_multi_index(0,
+                                     0,
+                                     m_thread_data_on_block_idx[I1],
+                                     n_thread_data_on_block_idx[I1],
+                                     m_thread_data_on_block_idx[I2],
+                                     n_thread_data_on_block_idx[I2],
+                                     n_thread_data_on_block_idx[I3],
+                                     n_thread_data_on_block_idx[I4]),
+                    tensor_operation::element_wise::Scale{rp_dropout}};
+
+            // shuffle: blockwise copy C from LDS to global
+            auto vgrad_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
+                ThisThreadBlock,            // ThreadGroup
+                CElementwiseOperation,      // ElementwiseOperation,
+                CGlobalMemoryDataOperation, // DstInMemOp,
+                Sequence<1,
+                         CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                         1,
+                         CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
+                CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
+                FloatCShuffle,        // typename SrcData,
+                OutputDataType,       // typename DstData,
+                decltype(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
+                decltype(vgrad_grid_desc_nblock_nperblock_oblock_operblock),
+                Sequence<0, 1, 2, 3>,                           // typename DimAccessOrder,
+                3,                                              // index_t VectorDim,
+                CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,
+                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
+                false> // bool ThreadTransferDstResetCoordinateAfterRun>
+                {c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(0, 0, 0, 0),
+                 vgrad_grid_desc_nblock_nperblock_oblock_operblock,
+                 make_multi_index(block_work_idx_n, 0, block_work_idx[I1], 0),
+                 c_element_op};
+
+            // shuffle: threadwise copy C from VGPR to LDS
+            auto kgrad_thread_copy_vgpr_to_lds =
+                ThreadwiseTensorSliceTransfer_v1r3<FloatGemmAcc,
+                                                   FloatCShuffle,
+                                                   decltype(c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4),
+                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4),
+                                                   SElementwiseOperation,
+                                                   Sequence<CShuffleMXdlPerWavePerShuffle,
+                                                            CShuffleNXdlPerWavePerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            I1,
+                                                            N2,
+                                                            I1,
+                                                            N4>,
+                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                                   7,
+                                                   1,
+                                                   InMemoryDataOperationEnum::Set,
+                                                   1,
+                                                   true>{
+                    c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                    make_multi_index(0,
+                                     0,
+                                     m_thread_data_on_block_idx[I1],
+                                     n_thread_data_on_block_idx[I1],
+                                     m_thread_data_on_block_idx[I2],
+                                     n_thread_data_on_block_idx[I2],
+                                     n_thread_data_on_block_idx[I3],
+                                     n_thread_data_on_block_idx[I4]),
+                    scale_rp_dropout};
+
+            // shuffle: blockwise copy C from LDS to global
+            auto kgrad_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
+                ThisThreadBlock,            // ThreadGroup
+                CElementwiseOperation,      // ElementwiseOperation,
+                CGlobalMemoryDataOperation, // DstInMemOp,
+                Sequence<1,
+                         CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                         1,
+                         CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
+                CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
+                FloatCShuffle,        // typename SrcData,
+                OutputDataType,       // typename DstData,
+                decltype(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
+                decltype(kgrad_grid_desc_nblock_nperblock_oblock_operblock),
+                Sequence<0, 1, 2, 3>,                           // typename DimAccessOrder,
+                3,                                              // index_t VectorDim,
+                CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,
+                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
+                false> // bool ThreadTransferDstResetCoordinateAfterRun>
+                {c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(0, 0, 0, 0),
+                 kgrad_grid_desc_nblock_nperblock_oblock_operblock,
+                 make_multi_index(block_work_idx_n, 0, block_work_idx[I1], 0),
+                 c_element_op};
+
+            // space filling curve for threadwise C in VGPR
+            constexpr auto sfc_c_vgpr =
+                SpaceFillingCurve<Sequence<NXdlPerWave, Gemm1NXdlPerWave, 1, 1, 1, N2, 1, N4>,
+                                  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                  Sequence<CShuffleMXdlPerWavePerShuffle,
+                                           CShuffleNXdlPerWavePerShuffle,
+                                           1,
+                                           1,
+                                           1,
+                                           N2,
+                                           1,
+                                           N4>>{};
+
+            // space filling curve for shuffled blockwise C in global mem
+            constexpr auto sfc_c_global =
+                SpaceFillingCurve<Sequence<1, NPerBlock, 1, Gemm1NPerBlock>,
+                                  Sequence<0, 2, 1, 3>,
+                                  Sequence<1,
+                                           CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                                           1,
+                                           CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
+
+            constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
+
+            static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");
+
+            // dK
+            static_for<0, num_access, 1>{}([&](auto access_id) {
+                // make sure it's safe to write to LDS
+                block_sync_lds();
+
+                // each thread write its data from VGPR to LDS
+                kgrad_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                                                  sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
+                                                  kgrad_thread_buf,
+                                                  c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                                                  c_shuffle_block_buf);
+
+                // make sure it's safe to read from LDS
+                block_sync_lds();
+
+                // each block copy its data from LDS to global
+                kgrad_shuffle_block_copy_lds_to_global.Run(
+                    c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                    c_shuffle_block_buf,
+                    kgrad_grid_desc_nblock_nperblock_oblock_operblock,
+                    kgrad_grid_buf);
+
+                if constexpr(access_id < num_access - 1)
+                {
+                    constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
+
+                    // move on C
+                    kgrad_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(
+                        kgrad_grid_desc_nblock_nperblock_oblock_operblock, c_global_step);
+                }
+            });
+
+            // dV
+            static_for<0, num_access, 1>{}([&](auto access_id) {
+                // make sure it's safe to write to LDS
+                block_sync_lds();
+
+                // each thread write its data from VGPR to LDS
+                vgrad_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                                                  sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
+                                                  vgrad_thread_buf,
+                                                  c_block_desc_m0_n0_m1_n1_m2_n2_n3_n4,
+                                                  c_shuffle_block_buf);
+
+                // make sure it's safe to read from LDS
+                block_sync_lds();
+
+                // each block copy its data from LDS to global
+                vgrad_shuffle_block_copy_lds_to_global.Run(
+                    c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                    c_shuffle_block_buf,
+                    vgrad_grid_desc_nblock_nperblock_oblock_operblock,
+                    vgrad_grid_buf);
+
+                if constexpr(access_id < num_access - 1)
+                {
+                    constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
+
+                    // move on C
+                    vgrad_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(
+                        vgrad_grid_desc_nblock_nperblock_oblock_operblock, c_global_step);
+                }
+            });
+        }
+    }
+};
+
+} // namespace ck