Merge remote-tracking branch 'origin/develop' into miopen_downstream-dynamic_reduction_pr

composable_kernel/include/problem_transform/transform_backward_weight_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw.hpp

@@ -0,0 +1,129 @@
+#ifndef CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R2_NCHW_KCYX_NKHW_HPP
+#define CK_TRANSFORM_BACKWARD_WEIGHT_CONVOLUTION_INTO_GEMM_V4R4R2_NCHW_KCYX_NKHW_HPP
+
+#include "common_header.hpp"
+#include "tensor_descriptor.hpp"
+#include "tensor_descriptor_helper.hpp"
+
+namespace ck {
+
+// GemmM = K
+// GemmK = N * Ho * Wo
+// GemmN = C * Y * X
+template <typename... Wei,
+          typename... In,
+          typename... Out,
+          typename ConvStrides,
+          typename ConvDilations,
+          typename InLeftPads,
+          typename InRightPads,
+          index_t GemmK1Value>
+__host__ __device__ constexpr auto
+transform_backward_weight_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
+    const TensorDescriptor<Wei...>& wei_k_c_y_x_grid_desc,
+    const TensorDescriptor<In...>& in_n_c_hi_wi_grid_desc,
+    const TensorDescriptor<Out...>& out_n_k_ho_wo_grid_desc,
+    const ConvStrides& conv_strides,
+    const ConvDilations& conv_dilations,
+    const InLeftPads& in_left_pads,
+    const InRightPads& in_right_pads,
+    Number<GemmK1Value>)
+{
+    constexpr auto I0 = Number<0>{};
+    constexpr auto I1 = Number<1>{};
+    constexpr auto I2 = Number<2>{};
+    constexpr auto I3 = Number<3>{};
+
+    constexpr auto GemmK1 = Number<GemmK1Value>{};
+
+    const auto N = in_n_c_hi_wi_grid_desc.GetLength(I0);
+    const auto C = in_n_c_hi_wi_grid_desc.GetLength(I1);
+    const auto K = out_n_k_ho_wo_grid_desc.GetLength(I1);
+
+    const auto Hi = in_n_c_hi_wi_grid_desc.GetLength(I2);
+    const auto Wi = in_n_c_hi_wi_grid_desc.GetLength(I3);
+
+    const auto Ho = out_n_k_ho_wo_grid_desc.GetLength(I2);
+    const auto Wo = out_n_k_ho_wo_grid_desc.GetLength(I3);
+
+    const auto Y = wei_k_c_y_x_grid_desc.GetLength(I2);
+    const auto X = wei_k_c_y_x_grid_desc.GetLength(I3);
+
+    const auto ConvStrideH = conv_strides[I0];
+    const auto ConvStrideW = conv_strides[I1];
+
+    const auto ConvDilationH = conv_dilations[I0];
+    const auto ConvDilationW = conv_dilations[I1];
+
+    const auto InLeftPadH = in_left_pads[I0];
+    const auto InLeftPadW = in_left_pads[I1];
+
+    const auto InRightPadH = in_right_pads[I0];
+    const auto InRightPadW = in_right_pads[I1];
+
+    const auto GemmM  = K;
+    const auto GemmN  = C * Y * X;
+    const auto GemmK  = N * Ho * Wo;
+    const auto GemmK0 = GemmK / GemmK1;
+
+    // weight tensor
+    const auto wei_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
+        make_naive_tensor_descriptor_packed(make_tuple(K, C * Y * X)),
+        make_tuple(make_pass_through_transform(K), make_pass_through_transform(C * Y * X)),
+        make_tuple(Sequence<0>{}, Sequence<1>{}),
+        make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+    // input tensor
+    const auto in_n_c_hip_wip_grid_desc = transform_tensor_descriptor(
+        in_n_c_hi_wi_grid_desc,
+        make_tuple(make_pass_through_transform(N),
+                   make_pass_through_transform(C),
+                   make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                   make_pad_transform(Wi, InLeftPadW, InRightPadW)),
+        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+    const auto in_n_c_y_ho_x_wo_grid_desc = transform_tensor_descriptor(
+        in_n_c_hip_wip_grid_desc,
+        make_tuple(make_pass_through_transform(N),
+                   make_pass_through_transform(C),
+                   make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                   make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW))),
+        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4, 5>{}));
+
+    const auto in_gemmk_gemmn_grid_desc =
+        transform_tensor_descriptor(in_n_c_y_ho_x_wo_grid_desc,
+                                    make_tuple(make_merge_transform(make_tuple(C, Y, X)),
+                                               make_merge_transform(make_tuple(N, Ho, Wo))),
+                                    make_tuple(Sequence<1, 2, 4>{}, Sequence<0, 3, 5>{}),
+                                    make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+    const auto in_gemmk0_gemmn_gemmk1_grid_desc =
+        transform_tensor_descriptor(in_gemmk_gemmn_grid_desc,
+                                    make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
+                                               make_pass_through_transform(GemmN)),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+    // output tensor
+    const auto out_gemmk_gemmm_grid_desc = transform_tensor_descriptor(
+        make_naive_tensor_descriptor_packed(make_tuple(N, K, Ho * Wo)),
+        make_tuple(make_pass_through_transform(K), make_merge_transform(make_tuple(N, Ho * Wo))),
+        make_tuple(Sequence<1>{}, Sequence<0, 2>{}),
+        make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+    const auto out_gemmk0_gemmm_gemmk1_grid_desc =
+        transform_tensor_descriptor(out_gemmk_gemmm_grid_desc,
+                                    make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1)),
+                                               make_pass_through_transform(GemmM)),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+    return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
+                      in_gemmk0_gemmn_gemmk1_grid_desc,
+                      wei_gemmm_gemmn_grid_desc);
+}
+
+} // namespace ck
+#endif

composable_kernel/include/tensor_description/multi_index_transform.hpp

@@ -1327,6 +1327,129 @@ struct Merge_v2r2_magic_division
     }
 };
 
+// Implementation of "Merge" transformation primitive that uses division and mod. It is supposed to
+// be used for low_lengths that are known at compile time and are power of 2, otherwise performance
+// will be very bad
+template <typename LowLengths>
+struct Merge_v3_division_mod
+{
+    static constexpr index_t NDimLow = LowLengths::Size();
+
+    using LowerIndex = MultiIndex<NDimLow>;
+    using UpperIndex = MultiIndex<1>;
+
+    using LowLengthsScan =
+        decltype(container_reverse_exclusive_scan(LowLengths{}, math::multiplies{}, Number<1>{}));
+
+    using UpLengths =
+        decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies{}, Number<1>{})));
+
+    LowLengths low_lengths_;
+    LowLengthsScan low_lengths_scan_;
+    UpLengths up_lengths_;
+
+    __host__ __device__ constexpr Merge_v3_division_mod() = default;
+
+    __host__ __device__ constexpr Merge_v3_division_mod(const LowLengths& low_lengths)
+        : low_lengths_{low_lengths},
+          low_lengths_scan_{
+              container_reverse_exclusive_scan(low_lengths, math::multiplies{}, Number<1>{})},
+          up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies{}, Number<1>{}))}
+    {
+        static_assert(LowerIndex::Size() == NDimLow, "wrong!");
+    }
+
+    __host__ __device__ static constexpr index_t GetNumOfLowerDimension() { return NDimLow; }
+
+    __host__ __device__ static constexpr index_t GetNumOfUpperDimension() { return 1; }
+
+    __host__ __device__ constexpr const auto& GetUpperLengths() const { return up_lengths_; }
+
+    template <typename LowIdx, typename UpIdx>
+    __host__ __device__ constexpr void CalculateLowerIndex(LowIdx& idx_low,
+                                                           const UpIdx& idx_up) const
+    {
+        static_assert(LowIdx::Size() == NDimLow && UpIdx::Size() == 1,
+                      "wrong! inconsistent # of dimension");
+
+        index_t tmp = idx_up[Number<0>{}];
+
+        // division and mod
+        static_for<0, NDimLow - 1, 1>{}([&](auto i) {
+            idx_low(i) = tmp / this->low_lengths_scan_[i];
+            tmp %= this->low_lengths_scan_[i];
+        });
+
+        idx_low(Number<NDimLow - 1>{}) = tmp;
+    }
+
+    template <typename LowIdxDiff,
+              typename UpIdxDiff,
+              typename LowIdx,
+              typename UpIdx,
+              index_t Hack>
+    __host__ __device__ void UpdateLowerIndex(LowIdxDiff& idx_diff_low,
+                                              const UpIdxDiff&,
+                                              LowIdx& idx_low,
+                                              const UpIdx& idx_up_new,
+                                              Number<Hack>) const
+    {
+        static_assert(LowIdxDiff::Size() == NDimLow && UpIdxDiff::Size() == 1 &&
+                          LowIdx::Size() == NDimLow && UpIdx::Size() == 1,
+                      "wrong! inconsistent # of dimension");
+
+        constexpr auto I0   = Number<0>{};
+        constexpr auto INm1 = Number<NDimLow - 1>{};
+
+        index_t tmp = idx_up_new[I0];
+
+        static_for<0, NDimLow - 1, 1>{}([&](auto i) {
+            const index_t tmp2 = idx_low[i];
+            idx_low(i)         = tmp / this->low_lengths_scan_[i];
+            idx_diff_low(i)    = idx_low[i] - tmp2;
+            tmp %= this->low_lengths_scan_[i];
+        });
+
+        const index_t tmp2 = idx_low[INm1];
+        idx_low(INm1)      = tmp;
+        idx_diff_low(INm1) = idx_low[INm1] - tmp2;
+    }
+
+    __host__ __device__ static constexpr bool IsLinearTransform() { return false; }
+
+    __host__ __device__ static constexpr bool IsValidUpperIndexAlwaysMappedToValidLowerIndex()
+    {
+        return true;
+    }
+
+    __host__ __device__ static constexpr bool IsKnownAtCompileTime()
+    {
+        return is_known_at_compile_time<LowLengths>::value &&
+               is_known_at_compile_time<LowLengthsScan>::value &&
+               is_known_at_compile_time<UpLengths>::value;
+    }
+
+    template <typename UpIdx>
+    __host__ __device__ static constexpr bool
+    IsValidUpperIndexMappedToValidLowerIndex(const UpIdx& /* idx_up */)
+    {
+        return true;
+    }
+
+    __host__ __device__ void Print() const
+    {
+        printf("{");
+        printf("Merge_v3_direct_division_mod, ");
+        printf("low_lengths_ ");
+        print_multi_index(low_lengths_);
+        printf("low_lengths_scan_ ");
+        print_multi_index(low_lengths_scan_);
+        printf("up_lengths_ ");
+        print_multi_index(up_lengths_);
+        printf("}");
+    }
+};
+
 template <typename UpLengths, bool Use24BitIntegerCalculation>
 struct UnMerge
 {

composable_kernel/include/tensor_description/multi_index_transform_helper.hpp

@@ -52,22 +52,36 @@ __host__ __device__ constexpr auto make_embed_transform(const UpLengths& up_leng
 template <typename LowLengths>
 __host__ __device__ constexpr auto make_merge_transform(const LowLengths& low_lengths)
 {
-#if !CK_EXPERIMENTAL_MERGE_USE_MAGIC_DIVISION
+#if CK_EXPERIMENTAL_MERGE_USE_MAGIC_DIVISION
+    return make_merge_transform_v2_magic_division(low_lengths);
+#else
+    return make_merge_transform_v1_carry_check(low_lengths);
+#endif
+}
+
+template <typename LowLengths>
+__host__ __device__ constexpr auto
+make_merge_transform_v1_carry_check(const LowLengths& low_lengths)
+{
     return Merge_v1_carry_check<LowLengths>{low_lengths};
-#else
-#if 1
-    return Merge_v2_magic_division<LowLengths>{low_lengths};
-#else
-    return Merge_v2r2_magic_division<LowLengths>{low_lengths};
-#endif
-#endif
 }
 
 template <typename LowLengths>
 __host__ __device__ constexpr auto
 make_merge_transform_v2_magic_division(const LowLengths& low_lengths)
 {
+#if 1
     return Merge_v2_magic_division<LowLengths>{low_lengths};
+#else
+    return Merge_v2r2_magic_division<LowLengths>{low_lengths};
+#endif
+}
+
+template <typename LowLengths>
+__host__ __device__ constexpr auto
+make_merge_transform_v3_division_mod(const LowLengths& low_lengths)
+{
+    return Merge_v3_division_mod<LowLengths>{low_lengths};
 }
 
 template <typename UpLengths, bool Use24BitIntegerCalculation = false>

composable_kernel/include/tensor_description/tensor_adaptor.hpp

@@ -189,8 +189,7 @@ struct TensorAdaptor
         bool is_known = true;
 
         static_for<0, Transforms::Size(), 1>{}([&](auto i) {
-            is_known &=
-                remove_cv_t<remove_reference_t<decltype(Transforms{}[i])>>::IsKnownAtCompileTime();
+            is_known &= remove_cvref_t<decltype(Transforms{}[i])>::IsKnownAtCompileTime();
         });
 
         return is_known && is_known_at_compile_time<ElementSize>::value;

composable_kernel/include/tensor_description/tensor_descriptor.hpp

@@ -185,8 +185,7 @@ struct TensorDescriptor
         bool is_known = true;
 
         static_for<0, Transforms::Size(), 1>{}([&](auto i) {
-            is_known &=
-                remove_cv_t<remove_reference_t<decltype(Transforms{}[i])>>::IsKnownAtCompileTime();
+            is_known &= remove_cvref_t<decltype(Transforms{}[i])>::IsKnownAtCompileTime();
         });
 
         return is_known && is_known_at_compile_time<ElementSize>::value &&
@@ -587,11 +586,11 @@ __host__ __device__ constexpr bool coordinate_has_valid_offset(const TensorDesc&
 
 template <typename TensorDesc>
 using TensorCoordinate_t = decltype(make_tensor_coordinate(
-    TensorDesc{}, MultiIndex<remove_cv_t<remove_reference_t<TensorDesc>>::GetNumOfDimension()>{}));
+    TensorDesc{}, MultiIndex<remove_cvref_t<TensorDesc>::GetNumOfDimension()>{}));
 
 template <typename TensorDesc>
 using TensorCoordinateStep_t = decltype(make_tensor_coordinate_step(
-    TensorDesc{}, MultiIndex<remove_cv_t<remove_reference_t<TensorDesc>>::GetNumOfDimension()>{}));
+    TensorDesc{}, MultiIndex<remove_cvref_t<TensorDesc>::GetNumOfDimension()>{}));
 
 } // namespace ck
 #endif

composable_kernel/include/tensor_operation/blockwise_gemm_dlops_v3.hpp

@@ -110,13 +110,11 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
                         const BThreadBuffer& b_thread_buf,
                         CThreadBuffer& c_thread_buf) const
     {
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename ABlockBuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatA>>>::value &&
-                      is_same<remove_cv_t<remove_reference_t<typename BThreadBuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatB>>>::value &&
-                      is_same<remove_cv_t<remove_reference_t<typename CThreadBuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatC>>>::value &&
-                      "wrong! inconsistent type");
+        static_assert(
+            is_same<remove_cvref_t<typename ABlockBuffer::type>, remove_cvref_t<FloatA>>::value &&
+            is_same<remove_cvref_t<typename BThreadBuffer::type>, remove_cvref_t<FloatB>>::value &&
+            is_same<remove_cvref_t<typename CThreadBuffer::type>, remove_cvref_t<FloatC>>::value &&
+            "wrong! inconsistent type");
 
         constexpr auto I0 = Number<0>{};
 

composable_kernel/include/tensor_operation/blockwise_gemm_xdlops.hpp

@@ -4,21 +4,21 @@
 #include "common_header.hpp"
 #include "threadwise_tensor_slice_transfer.hpp"
 #include "xdlops_gemm.hpp"
+#include "tensor_adaptor.hpp"
 
 namespace ck {
 
 template <index_t BlockSize,
           typename FloatAB,
-          class ABlockDesc,
-          class BBlockDesc,
-          index_t MPerWave,
-          index_t NPerWave,
+          typename AK0MK1BlockDesc,
+          typename BK0NK1BlockDesc,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
           index_t K1>
-struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1
+struct BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1
 {
-
-    using CIndex = MultiIndex<2>;
-
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
     static constexpr auto I2 = Number<2>{};
@@ -26,111 +26,165 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1
 
     static constexpr index_t WaveSize = 64;
 
-    static constexpr index_t M0 = ABlockDesc{}.GetLength(I1);
-    static constexpr index_t M1 = ABlockDesc{}.GetLength(I2);
+    static constexpr index_t MPerBlock = AK0MK1BlockDesc{}.GetLength(I1);
+    static constexpr index_t NPerBlock = BK0NK1BlockDesc{}.GetLength(I1);
 
-    static constexpr index_t N0 = BBlockDesc{}.GetLength(I1);
-    static constexpr index_t N1 = BBlockDesc{}.GetLength(I2);
+    static constexpr index_t K0        = BK0NK1BlockDesc{}.GetLength(I0);
+    static constexpr index_t KPerBlock = K0;
 
-    static constexpr auto xdlops_gemm = XdlopsGemm<FloatAB, MPerWave, NPerWave, K1>{};
+    static constexpr auto xdlops_gemm = XdlopsGemm<FloatAB, MPerXDL, NPerXDL, K1>{};
 
-    static constexpr index_t MWaves = M1 / MPerWave;
-    static constexpr index_t NWaves = N1 / NPerWave;
+    static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerXDL);
+    static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);
 
-    static constexpr index_t MRepeat = M0;
-    static constexpr index_t NRepeat = N0;
+    __device__ static auto GetWaveIdx()
+    {
+        const index_t thread_id = get_thread_local_1d_id();
 
-    __device__ constexpr auto GetCLayout() const { return xdlops_gemm.GetCLayout(); }
+        constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
+            make_tuple(Sequence<0, 1, 2>{}),
+            make_tuple(Sequence<0>{}));
 
-    __device__ constexpr auto GetNumBlks() const { return xdlops_gemm.GetCLayout().GetNumBlks(); }
-
-    __device__ constexpr auto GetBlkSize() const { return xdlops_gemm.GetCLayout().GetBlkSize(); }
+        return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
+    }
 
     __device__ static auto CalculateAThreadOriginDataIndex()
     {
-        const index_t thread_id = get_thread_local_1d_id();
-        const index_t waveId    = thread_id / WaveSize;
-        const index_t laneId    = thread_id % WaveSize;
-        const index_t waveId_m  = waveId / NWaves;
+        const auto wave_idx = GetWaveIdx();
 
-        if constexpr(xdlops_gemm.IsKReduction)
-        {
-            const index_t m_offset = waveId_m * MPerWave + xdlops_gemm.GetBlkTd(laneId);
-            const index_t k_offset = xdlops_gemm.GetBlkId(laneId);
-            return make_tuple(k_offset, 0, m_offset, 0);
-        }
-        else
-        {
-            const index_t m_offset = waveId_m * MPerWave + laneId;
-            const index_t k_offset = 0;
-            return make_tuple(k_offset, 0, m_offset, 0);
-        }
+        const auto waveId_m = wave_idx[I0];
+
+        const auto xdlops_a_idx = xdlops_gemm.CalculateAThreadOriginDataIndex();
+
+        return make_tuple(xdlops_a_idx[I0], 0, waveId_m, xdlops_a_idx[I1], 0);
     }
 
     __device__ static auto CalculateBThreadOriginDataIndex()
     {
-        const index_t thread_id = get_thread_local_1d_id();
-        const index_t waveId    = thread_id / WaveSize;
-        const index_t laneId    = thread_id % WaveSize;
-        const index_t waveId_n  = waveId % NWaves;
+        const auto wave_idx = GetWaveIdx();
 
-        if constexpr(xdlops_gemm.IsKReduction)
-        {
-            const index_t n_offset = waveId_n * NPerWave + xdlops_gemm.GetBlkTd(laneId);
-            const index_t k_offset = xdlops_gemm.GetBlkId(laneId);
-            return make_tuple(k_offset, 0, n_offset, 0);
-        }
-        else
-        {
-            const index_t n_offset = waveId_n * NPerWave + laneId;
-            const index_t k_offset = 0;
-            return make_tuple(k_offset, 0, n_offset, 0);
-        }
+        const auto waveId_n = wave_idx[I1];
+
+        const auto xdlops_b_idx = xdlops_gemm.CalculateBThreadOriginDataIndex();
+
+        return make_tuple(xdlops_b_idx[I0], 0, waveId_n, xdlops_b_idx[I1], 0);
     }
 
     template <index_t m0, index_t n0, index_t xdlops_i, index_t blk_i>
-    __device__ static CIndex
+    __device__ static auto
         CalculateCThreadOriginDataIndex(Number<m0>, Number<n0>, Number<xdlops_i>, Number<blk_i>)
     {
+        const auto wave_idx = GetWaveIdx();
 
-        const index_t waveId = get_thread_local_1d_id() / WaveSize;
+        const auto waveId_m = wave_idx[I0];
+        const auto waveId_n = wave_idx[I1];
 
-        const auto thread_mtx_on_blk = xdlops_gemm.GetBeginOfThreadBlk(xdlops_i, blk_i);
+        const auto blk_idx = xdlops_gemm.GetBeginOfThreadBlk(xdlops_i, blk_i);
 
-        const index_t waveId_m = waveId / NWaves;
-        const index_t waveId_n = waveId % NWaves;
+        constexpr auto mrepeat_mwave_mperxdl_to_m_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, MPerXDL))),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0, 1, 2>{}));
 
-        const index_t m_offset = m0 * M1 + waveId_m * MPerWave + thread_mtx_on_blk[I0];
-        const index_t n_offset = n0 * N1 + waveId_n * NPerWave + thread_mtx_on_blk[I1];
+        constexpr auto nrepeat_nwave_nperxdl_to_n_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_unmerge_transform(make_tuple(NRepeat, NWaves, NPerXDL))),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0, 1, 2>{}));
 
-        return CIndex{m_offset, n_offset};
+        const index_t c_thread_m = mrepeat_mwave_mperxdl_to_m_adaptor.CalculateBottomIndex(
+            make_tuple(m0, waveId_m, blk_idx[I0]))[I0];
+        const index_t c_thread_n = nrepeat_nwave_nperxdl_to_n_adaptor.CalculateBottomIndex(
+            make_tuple(n0, waveId_n, blk_idx[I1]))[I0];
+
+        return make_tuple(c_thread_m, c_thread_n);
     }
 
-    __device__ BlockwiseGemmXdlops_km_kn_m0m1m2n_v1()
-        : a_thread_copy_{CalculateAThreadOriginDataIndex()},
-          b_thread_copy_{CalculateBThreadOriginDataIndex()}
+    __host__ __device__ BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1()
     {
-        static_assert(ABlockDesc::IsKnownAtCompileTime() && BBlockDesc::IsKnownAtCompileTime(),
+        static_assert(AK0MK1BlockDesc::IsKnownAtCompileTime() &&
+                          BK0NK1BlockDesc::IsKnownAtCompileTime(),
                       "wrong! Desc should be known at compile-time");
 
-        static_assert(ABlockDesc{}.GetLength(I0) == BBlockDesc{}.GetLength(I0),
-                      "wrong! K dimension not consistent");
+        static_assert(AK0MK1BlockDesc{}.GetLength(I0) == BK0NK1BlockDesc{}.GetLength(I0),
+                      "wrong! K0 dimension not consistent");
 
-        static_assert(ABlockDesc{}.GetLength(I3) == BBlockDesc{}.GetLength(I3),
+        static_assert(AK0MK1BlockDesc{}.GetLength(I2) == BK0NK1BlockDesc{}.GetLength(I2),
                       "wrong! K1 dimension not consistent");
 
         static_assert(BlockSize == MWaves * NWaves * WaveSize,
                       "BlockSize != MWaves * NWaves * WaveSize\n");
 
-        static_assert(K1 == BBlockDesc{}.GetLength(I3), "K1 is wrong!");
-
-        constexpr index_t KPerBlock = ABlockDesc{}.GetLength(I0);
-
-        static_assert(KPerBlock % xdlops_gemm.KPerXdlops == 0, "KPerBlock is wrong!");
-
-        static_assert(K1 % xdlops_gemm.mfma_type.k_base == 0, "K1 is wrong!");
+        static_assert(MPerBlock % (MPerXDL * MRepeat) == 0 && NPerBlock % (NPerXDL * NRepeat) == 0,
+                      "wrong!");
     }
 
+    __host__ __device__ static constexpr auto GetCM0N0M1N1M2M3M4N2ThreadDescriptor()
+    {
+        constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
+
+        constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
+        constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
+        constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
+        constexpr auto N  = c_m0_m1_m2_n_tblk_lens[I3];
+
+        return make_naive_tensor_descriptor_packed(make_tuple(I1, I1, I1, I1, M0, M1, M2, N));
+    }
+
+    __host__ __device__ static constexpr auto GetCM0N0M1N1M2M3M4N2BlockDescriptor()
+    {
+        constexpr auto c_m0_n0_m1_n1_m2_n2_block_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
+                                                           Number<NRepeat>{},
+                                                           Number<MWaves>{},
+                                                           Number<NWaves>{},
+                                                           Number<MPerXDL>{},
+                                                           Number<NPerXDL>{}));
+
+        return xdlops_gemm.MakeCM0N0M1N1M2M3M4N2Descriptor(c_m0_n0_m1_n1_m2_n2_block_desc);
+    }
+
+    template <typename CMNGridDesc>
+    __host__ __device__ static constexpr auto
+    MakeCM0N0M1N1M2M3M4N2GridDescriptor(const CMNGridDesc& c_m_n_grid_desc)
+    {
+        const auto c_m0_n0_m1_n1_m2_n2_grid_desc = transform_tensor_descriptor(
+            c_m_n_grid_desc,
+            make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, MPerXDL)),
+                       make_unmerge_transform(make_tuple(NRepeat, NWaves, NPerXDL))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}));
+
+        return xdlops_gemm.MakeCM0N0M1N1M2M3M4N2Descriptor(c_m0_n0_m1_n1_m2_n2_grid_desc);
+    }
+
+    __host__ __device__ static constexpr auto MakeAK0M0M1M2K1BlockDescriptor()
+    {
+        return transform_tensor_descriptor(
+            AK0MK1BlockDesc{},
+            make_tuple(make_pass_through_transform(Number<KPerBlock>{}),
+                       make_unmerge_transform(
+                           make_tuple(Number<MRepeat>{}, Number<MWaves>{}, Number<MPerXDL>{})),
+                       make_pass_through_transform(Number<K1>{})),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+            make_tuple(Sequence<0>{}, Sequence<1, 2, 3>{}, Sequence<4>{}));
+    }
+
+    __host__ __device__ static constexpr auto MakeBK0N0N1N2K1BlockDescriptor()
+    {
+        return transform_tensor_descriptor(
+            BK0NK1BlockDesc{},
+            make_tuple(make_pass_through_transform(Number<KPerBlock>{}),
+                       make_unmerge_transform(
+                           make_tuple(Number<NRepeat>{}, Number<NWaves>{}, Number<NPerXDL>{})),
+                       make_pass_through_transform(Number<K1>{})),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+            make_tuple(Sequence<0>{}, Sequence<1, 2, 3>{}, Sequence<4>{}));
+    }
+
+    static constexpr auto a_k0_m0_m1_m2_k1_block_desc = MakeAK0M0M1M2K1BlockDescriptor();
+    static constexpr auto b_k0_n0_n1_n2_k1_block_desc = MakeBK0N0N1N2K1BlockDescriptor();
+
     template <typename ABlockBuffer, typename BBlockBuffer, typename CThreadBuffer>
     __device__ void Run(const ABlockBuffer& a_block_buf,
                         const BBlockBuffer& b_block_buf,
@@ -141,49 +195,48 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1
         auto b_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatAB>(
             b_thread_desc_.GetElementSpaceSize());
 
-        constexpr index_t KPerBlock = ABlockDesc{}.GetLength(I0);
+        vector_type<FloatAB, K1> a_thread_vec;
 
-        vector_type<FloatAB, a_thread_desc_.GetElementSpaceSize()> a_thread_vec;
+        vector_type<FloatAB, K1> b_thread_vec;
 
-        vector_type<FloatAB, b_thread_desc_.GetElementSpaceSize()> b_thread_vec;
-
-        static_for<0, KPerBlock, xdlops_gemm.KPerXdlops>{}([&](auto k) {
+        static_for<0, KPerBlock, xdlops_gemm.KPerXdlops / xdlops_gemm.KPerThread>{}([&](auto k0) {
             // read A
-            a_thread_copy_.Run(ABlockDesc{},
-                               make_tuple(k, I0, I0, I0),
+            a_thread_copy_.Run(a_k0_m0_m1_m2_k1_block_desc,
+                               make_tuple(k0, I0, I0, I0, I0),
                                a_block_buf,
                                a_thread_desc_,
-                               make_tuple(I0, I0, I0, I0),
+                               make_tuple(I0, I0, I0, I0, I0),
                                a_thread_buf);
 
             // read B
-            b_thread_copy_.Run(BBlockDesc{},
-                               make_tuple(k, I0, I0, I0),
+            b_thread_copy_.Run(b_k0_n0_n1_n2_k1_block_desc,
+                               make_tuple(k0, I0, I0, I0, I0),
                                b_block_buf,
                                b_thread_desc_,
-                               make_tuple(I0, I0, I0, I0),
+                               make_tuple(I0, I0, I0, I0, I0),
                                b_thread_buf);
 
-            using mfma_input_type =
-                typename vector_type<FloatAB, xdlops_gemm.mfma_type.k_base>::type;
-
-            static_for<0, a_thread_desc_.GetElementSpaceSize(), 1>{}([&](auto i) {
-                a_thread_vec.template AsType<FloatAB>()(Number<i>{}) = a_thread_buf[Number<i>{}];
-            });
-
-            static_for<0, b_thread_desc_.GetElementSpaceSize(), 1>{}([&](auto i) {
-                b_thread_vec.template AsType<FloatAB>()(Number<i>{}) = b_thread_buf[Number<i>{}];
-            });
+            using mfma_input_type = typename vector_type<FloatAB, xdlops_gemm.KPerThread>::type;
 
             static_for<0, MRepeat, 1>{}([&](auto m0) {
                 static_for<0, NRepeat, 1>{}([&](auto n0) {
-                    xdlops_gemm.template Run<decltype(a_thread_desc_),
-                                             decltype(b_thread_desc_),
-                                             decltype(c_thread_desc_),
-                                             m0,
-                                             n0>(a_thread_vec.template AsType<mfma_input_type>(),
-                                                 b_thread_vec.template AsType<mfma_input_type>(),
-                                                 c_thread_buf);
+                    static_for<0, K1, 1>{}([&](auto i) {
+                        a_thread_vec.template AsType<FloatAB>()(i) = a_thread_buf
+                            [Number<a_thread_desc_.CalculateOffset(make_tuple(0, m0, 0, 0, i))>{}];
+                    });
+
+                    static_for<0, K1, 1>{}([&](auto i) {
+                        b_thread_vec.template AsType<FloatAB>()(i) = b_thread_buf
+                            [Number<b_thread_desc_.CalculateOffset(make_tuple(0, n0, 0, 0, i))>{}];
+                    });
+
+                    constexpr index_t c_offset =
+                        c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                    xdlops_gemm.template Run<c_offset>(
+                        a_thread_vec.template AsType<mfma_input_type>(),
+                        b_thread_vec.template AsType<mfma_input_type>(),
+                        c_thread_buf);
                 });
             });
         });
@@ -191,333 +244,38 @@ struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1
 
     private:
     // A[K, M]
-    static constexpr auto a_thread_desc_ =
-        make_naive_tensor_descriptor_packed(make_tuple(I1, Number<MRepeat>{}, I1, Number<K1>{}));
+    static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor_packed(
+        make_tuple(I1, Number<MRepeat>{}, I1, I1, Number<K1>{}));
 
     // B[K, N]
-    static constexpr auto b_thread_desc_ =
-        make_naive_tensor_descriptor_packed(make_tuple(I1, Number<NRepeat>{}, I1, Number<K1>{}));
+    static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor_packed(
+        make_tuple(I1, Number<NRepeat>{}, I1, I1, Number<K1>{}));
 
-    static constexpr auto c_thread_desc_ =
-        make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{}, Number<NRepeat>{}));
+    static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(
+        make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, Number<xdlops_gemm.GetNumXdlops()>{}));
 
     using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatAB,
                                                          FloatAB,
-                                                         ABlockDesc,
+                                                         decltype(a_k0_m0_m1_m2_k1_block_desc),
                                                          decltype(a_thread_desc_),
-                                                         Sequence<1, MRepeat, 1, K1>,
-                                                         Sequence<0, 1, 2, 3>,
-                                                         3,
+                                                         Sequence<1, MRepeat, 1, 1, K1>,
+                                                         Sequence<0, 1, 2, 3, 4>,
+                                                         4,
                                                          K1,
                                                          1>;
 
     using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatAB,
                                                          FloatAB,
-                                                         BBlockDesc,
+                                                         decltype(b_k0_n0_n1_n2_k1_block_desc),
                                                          decltype(b_thread_desc_),
-                                                         Sequence<1, NRepeat, 1, K1>,
-                                                         Sequence<0, 1, 2, 3>,
-                                                         3,
+                                                         Sequence<1, NRepeat, 1, 1, K1>,
+                                                         Sequence<0, 1, 2, 3, 4>,
+                                                         4,
                                                          K1,
                                                          1>;
 
-    AThreadCopy a_thread_copy_;
-    BThreadCopy b_thread_copy_;
-};
-
-template <index_t BlockSize,
-          typename FloatAB,
-          class ABlockDesc,
-          class BBlockDesc,
-          index_t MPerWave,
-          index_t NPerWave,
-          index_t K1>
-struct BlockwiseGemmXdlops_km_kn_m0m1m2n_v1_2x2pipeline
-{
-
-    using CIndex = MultiIndex<2>;
-
-    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 xdlops_gemm = XdlopsGemm<float, MPerWave, NPerWave, K1>{};
-
-    static constexpr index_t WaveSize = 64;
-
-    static constexpr index_t M0 = ABlockDesc{}.GetLength(I1);
-    static constexpr index_t M1 = ABlockDesc{}.GetLength(I2);
-
-    static constexpr index_t N0 = BBlockDesc{}.GetLength(I1);
-    static constexpr index_t N1 = BBlockDesc{}.GetLength(I2);
-
-    static constexpr index_t MWaves = M1 / MPerWave;
-    static constexpr index_t NWaves = N1 / NPerWave;
-
-    static constexpr index_t MRepeat = M0;
-    static constexpr index_t NRepeat = N0;
-
-    __device__ constexpr auto GetCLayout() const { return xdlops_gemm.GetCLayout(); }
-
-    __device__ constexpr auto GetNumBlks() const { return xdlops_gemm.GetCLayout().GetNumBlks(); }
-
-    __device__ constexpr auto GetBlkSize() const { return xdlops_gemm.GetCLayout().GetBlkSize(); }
-
-    __device__ static auto CalculateAThreadOriginDataIndex()
-    {
-        const index_t thread_id = get_thread_local_1d_id();
-        const index_t waveId    = thread_id / WaveSize;
-        const index_t laneId    = thread_id % WaveSize;
-        const index_t waveId_m  = waveId / NWaves;
-
-        if constexpr(xdlops_gemm.IsKReduction)
-        {
-            const index_t m_offset = waveId_m * MPerWave + xdlops_gemm.GetBlkTd(laneId);
-            const index_t k_offset = xdlops_gemm.GetBlkId(laneId);
-            return make_tuple(k_offset, 0, m_offset, 0);
-        }
-        else
-        {
-            const index_t m_offset = waveId_m * MPerWave + laneId;
-            const index_t k_offset = 0;
-            return make_tuple(k_offset, 0, m_offset, 0);
-        }
-    }
-
-    __device__ static auto CalculateBThreadOriginDataIndex()
-    {
-        const index_t thread_id = get_thread_local_1d_id();
-        const index_t waveId    = thread_id / WaveSize;
-        const index_t laneId    = thread_id % WaveSize;
-        const index_t waveId_n  = waveId % NWaves;
-
-        if constexpr(xdlops_gemm.IsKReduction)
-        {
-            const index_t n_offset = waveId_n * NPerWave + xdlops_gemm.GetBlkTd(laneId);
-            const index_t k_offset = xdlops_gemm.GetBlkId(laneId);
-            return make_tuple(k_offset, 0, n_offset, 0);
-        }
-        else
-        {
-            const index_t n_offset = waveId_n * NPerWave + laneId;
-            const index_t k_offset = 0;
-            return make_tuple(k_offset, 0, n_offset, 0);
-        }
-    }
-
-    template <index_t m0, index_t n0, index_t xdlops_i, index_t blk_i>
-    __device__ static CIndex
-        CalculateCThreadOriginDataIndex(Number<m0>, Number<n0>, Number<xdlops_i>, Number<blk_i>)
-    {
-
-        const index_t waveId = get_thread_local_1d_id() / WaveSize;
-
-        const auto thread_mtx_on_blk = xdlops_gemm.GetBeginOfThreadBlk(xdlops_i, blk_i);
-
-        const index_t waveId_m = waveId / NWaves;
-        const index_t waveId_n = waveId % NWaves;
-
-        const index_t m_offset = m0 * M1 + waveId_m * MPerWave + thread_mtx_on_blk[I0];
-        const index_t n_offset = n0 * N1 + waveId_n * NPerWave + thread_mtx_on_blk[I1];
-
-        return CIndex{m_offset, n_offset};
-    }
-
-    __device__ BlockwiseGemmXdlops_km_kn_m0m1m2n_v1_2x2pipeline()
-        : a_thread_copy_{CalculateAThreadOriginDataIndex()},
-          b_thread_copy_{CalculateBThreadOriginDataIndex()}
-    {
-        static_assert(ABlockDesc::IsKnownAtCompileTime() && BBlockDesc::IsKnownAtCompileTime(),
-                      "wrong! Desc should be known at compile-time");
-
-        static_assert(ABlockDesc{}.GetLength(I0) == BBlockDesc{}.GetLength(I0),
-                      "wrong! K dimension not consistent");
-
-        static_assert(ABlockDesc{}.GetLength(I3) == BBlockDesc{}.GetLength(I3),
-                      "wrong! K1 dimension not consistent");
-
-        static_assert(BlockSize == MWaves * NWaves * WaveSize,
-                      "BlockSize != MWaves * NWaves * WaveSize\n");
-
-        static_assert(K1 == BBlockDesc{}.GetLength(I3), "K1 is wrong!");
-
-        constexpr index_t KPerBlock = ABlockDesc{}.GetLength(I0);
-
-        static_assert(KPerBlock % xdlops_gemm.KPerXdlops == 0, "KPerBlock is wrong!");
-
-        static_assert(K1 % xdlops_gemm.mfma_type.k_base == 0, "K1 is wrong!");
-    }
-
-    template <typename ABlockBuffer, typename BBlockBuffer, typename CThreadBuffer>
-    __device__ void Run(const ABlockBuffer& a_block_buf,
-                        const BBlockBuffer& b_block_buf,
-                        CThreadBuffer& c_thread_buf) const
-    {
-        auto a_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatAB>(
-            a_thread_desc_.GetElementSpaceSize());
-        auto b_thread_buf = make_static_buffer<AddressSpaceEnum_t::Vgpr, FloatAB>(
-            b_thread_desc_.GetElementSpaceSize());
-
-        constexpr index_t KPerBlock = ABlockDesc{}.GetLength(I0);
-
-        // read A_sub_0
-        a_thread_copy_.Run(ABlockDesc{},
-                           make_tuple(I0, I0, I0, I0),
-                           a_block_buf,
-                           a_thread_desc_,
-                           make_tuple(I0, I0, I0, I0),
-                           a_thread_buf);
-
-        // read B_sub_0
-        b_thread_copy_.Run(BBlockDesc{},
-                           make_tuple(I0, I0, I0, I0),
-                           b_block_buf,
-                           b_thread_desc_,
-                           make_tuple(I0, I0, I0, I0),
-                           b_thread_buf);
-
-        // read B_sub_1
-        b_thread_copy_.Run(BBlockDesc{},
-                           make_tuple(I0, I1, I0, I0),
-                           b_block_buf,
-                           b_thread_desc_,
-                           make_tuple(I0, I1, I0, I0),
-                           b_thread_buf);
-
-        // read A_sub_1
-        a_thread_copy_.Run(ABlockDesc{},
-                           make_tuple(I0, I1, I0, I0),
-                           a_block_buf,
-                           a_thread_desc_,
-                           make_tuple(I0, I1, I0, I0),
-                           a_thread_buf);
-
-        // C_sub_00 += transpose(A_sub_0) * B_sub_0
-        xdlops_gemm.template Run<decltype(a_thread_desc_),
-                                 decltype(b_thread_desc_),
-                                 decltype(c_thread_desc_),
-                                 0,
-                                 0>(a_thread_buf, b_thread_buf, c_thread_buf);
-
-        // C_sub_01 += transpose(A_sub_0) * B_sub_1
-        xdlops_gemm.template Run<decltype(a_thread_desc_),
-                                 decltype(b_thread_desc_),
-                                 decltype(c_thread_desc_),
-                                 0,
-                                 1>(a_thread_buf, b_thread_buf, c_thread_buf);
-
-        static_for<xdlops_gemm.KPerXdlops, KPerBlock, xdlops_gemm.KPerXdlops>{}([&](auto k) {
-            // read A_sub_0
-            a_thread_copy_.Run(ABlockDesc{},
-                               make_tuple(k, I0, I0, I0),
-                               a_block_buf,
-                               a_thread_desc_,
-                               make_tuple(I0, I0, I0, I0),
-                               a_thread_buf);
-
-            // C_sub_10 += transpose(A_sub_1) * B_sub_0
-            xdlops_gemm.template Run<decltype(a_thread_desc_),
-                                     decltype(b_thread_desc_),
-                                     decltype(c_thread_desc_),
-                                     1,
-                                     0>(a_thread_buf, b_thread_buf, c_thread_buf);
-
-            // read B_sub_0
-            b_thread_copy_.Run(BBlockDesc{},
-                               make_tuple(k, I0, I0, I0),
-                               b_block_buf,
-                               b_thread_desc_,
-                               make_tuple(I0, I0, I0, I0),
-                               b_thread_buf);
-
-            // C_sub_11 += transpose(A_sub_1) * B_sub_1
-            xdlops_gemm.template Run<decltype(a_thread_desc_),
-                                     decltype(b_thread_desc_),
-                                     decltype(c_thread_desc_),
-                                     1,
-                                     1>(a_thread_buf, b_thread_buf, c_thread_buf);
-
-            // read B_sub_1
-            b_thread_copy_.Run(BBlockDesc{},
-                               make_tuple(k, I1, I0, I0),
-                               b_block_buf,
-                               b_thread_desc_,
-                               make_tuple(I0, I1, I0, I0),
-                               b_thread_buf);
-
-            // read A_sub_1
-            a_thread_copy_.Run(ABlockDesc{},
-                               make_tuple(k, I1, I0, I0),
-                               a_block_buf,
-                               a_thread_desc_,
-                               make_tuple(I0, I1, I0, I0),
-                               a_thread_buf);
-
-            // C_sub_00 += transpose(A_sub_0) * B_sub_0
-            xdlops_gemm.template Run<decltype(a_thread_desc_),
-                                     decltype(b_thread_desc_),
-                                     decltype(c_thread_desc_),
-                                     0,
-                                     0>(a_thread_buf, b_thread_buf, c_thread_buf);
-
-            // C_sub_01 += transpose(A_sub_0) * B_sub_1
-            xdlops_gemm.template Run<decltype(a_thread_desc_),
-                                     decltype(b_thread_desc_),
-                                     decltype(c_thread_desc_),
-                                     0,
-                                     1>(a_thread_buf, b_thread_buf, c_thread_buf);
-        });
-
-        // C_sub_10 += transpose(A_sub_1) * B_sub_0
-        xdlops_gemm.template Run<decltype(a_thread_desc_),
-                                 decltype(b_thread_desc_),
-                                 decltype(c_thread_desc_),
-                                 1,
-                                 0>(a_thread_buf, b_thread_buf, c_thread_buf);
-
-        // C_sub_11 += transpose(A_sub_1) * B_sub_1
-        xdlops_gemm.template Run<decltype(a_thread_desc_),
-                                 decltype(b_thread_desc_),
-                                 decltype(c_thread_desc_),
-                                 1,
-                                 1>(a_thread_buf, b_thread_buf, c_thread_buf);
-    }
-
-    private:
-    // A[K, M]
-    static constexpr auto a_thread_desc_ =
-        make_naive_tensor_descriptor_packed(make_tuple(I1, Number<MRepeat>{}, I1, Number<K1>{}));
-
-    // B[K, N]
-    static constexpr auto b_thread_desc_ =
-        make_naive_tensor_descriptor_packed(make_tuple(I1, Number<NRepeat>{}, I1, Number<K1>{}));
-
-    static constexpr auto c_thread_desc_ =
-        make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{}, Number<NRepeat>{}));
-
-    using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatAB,
-                                                         FloatAB,
-                                                         ABlockDesc,
-                                                         decltype(a_thread_desc_),
-                                                         Sequence<1, 1, 1, K1>,
-                                                         Sequence<0, 1, 2, 3>,
-                                                         3,
-                                                         1, // K1,
-                                                         1>;
-
-    using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<FloatAB,
-                                                         FloatAB,
-                                                         BBlockDesc,
-                                                         decltype(b_thread_desc_),
-                                                         Sequence<1, 1, 1, K1>,
-                                                         Sequence<0, 1, 2, 3>,
-                                                         3,
-                                                         1, // K1,
-                                                         1>;
-
-    AThreadCopy a_thread_copy_;
-    BThreadCopy b_thread_copy_;
+    AThreadCopy a_thread_copy_{CalculateAThreadOriginDataIndex()};
+    BThreadCopy b_thread_copy_{CalculateBThreadOriginDataIndex()};
 };
 
 } // namespace ck

composable_kernel/include/tensor_operation/gridwise_gemm_xdlops_v2r3.hpp

@@ -18,7 +18,7 @@ template <typename GridwiseGemm,
           typename FloatC,
           typename AK0MK1GridDesc,
           typename BK0NK1GridDesc,
-          typename CM0M1M2NGridDesc,
+          typename CM0N0M1N1M2M3M4N2GridDesc,
           typename CBlockClusterAdaptor>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
@@ -29,7 +29,7 @@ __global__ void
                                 FloatC* __restrict__ p_c_grid,
                                 const AK0MK1GridDesc a_k0_m_k1_grid_desc,
                                 const BK0NK1GridDesc b_k0_n_k1_grid_desc,
-                                const CM0M1M2NGridDesc c_m0_m1_m2_n_grid_desc,
+                                const CM0N0M1N1M2M3M4N2GridDesc c_m0_m1_m2_n_grid_desc,
                                 const CBlockClusterAdaptor c_block_cluster_adaptor)
 {
     constexpr index_t shared_block_size =
@@ -43,7 +43,7 @@ __global__ void
                       p_shared_block,
                       a_k0_m_k1_grid_desc,
                       b_k0_n_k1_grid_desc,
-                      c_m0_m1_m2_n_grid_desc,
+                      c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                       c_block_cluster_adaptor);
 }
 #elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
@@ -52,7 +52,7 @@ template <typename GridwiseGemm,
           typename FloatC,
           typename AK0MK1GridDesc,
           typename BK0NK1GridDesc,
-          typename CM0M1M2NGridDesc,
+          typename CM0N0M1N1M2M3M4N2GridDesc,
           typename CBlockClusterAdaptor>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
@@ -63,7 +63,7 @@ __global__ void
                                 FloatC* __restrict__ p_c_grid,
                                 const void CONSTANT* p_a_k0_m_k1_grid_desc,
                                 const void CONSTANT* p_b_k0_n_k1_grid_desc,
-                                const void CONSTANT* p_c_m0_m1_m2_n_grid_desc,
+                                const void CONSTANT* p_c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                                 const void CONSTANT* p_c_block_cluster_adaptor)
 {
     constexpr index_t shared_block_size =
@@ -73,8 +73,9 @@ __global__ void
         cast_pointer_to_generic_address_space(p_a_k0_m_k1_grid_desc));
     const auto b_k0_n_k1_grid_desc = *reinterpret_cast<const BK0NK1GridDesc*>(
         cast_pointer_to_generic_address_space(p_b_k0_n_k1_grid_desc));
-    const auto c_m0_m1_m2_n_grid_desc = *reinterpret_cast<const CM0M1M2NGridDesc*>(
-        cast_pointer_to_generic_address_space(p_c_m0_m1_m2_n_grid_desc));
+    const auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc =
+        *reinterpret_cast<const CM0N0M1N1M2M3M4N2GridDesc*>(
+            cast_pointer_to_generic_address_space(p_c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc));
     const auto c_block_cluster_adaptor = *reinterpret_cast<const CBlockClusterAdaptor*>(
         cast_pointer_to_generic_address_space(p_c_block_cluster_adaptor));
 
@@ -86,7 +87,7 @@ __global__ void
                       p_shared_block,
                       a_k0_m_k1_grid_desc,
                       b_k0_n_k1_grid_desc,
-                      c_m0_m1_m2_n_grid_desc,
+                      c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                       c_block_cluster_adaptor);
 }
 #endif
@@ -102,8 +103,8 @@ template <index_t BlockSize,
           index_t MPerBlock,
           index_t NPerBlock,
           index_t KPerBlock,
-          index_t MPerWave,
-          index_t NPerWave,
+          index_t MPerXDL,
+          index_t NPerXDL,
           index_t K1Value,
           index_t MRepeat,
           index_t NRepeat,
@@ -138,6 +139,9 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
     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>{};
 
     // K1 should be Number<...>
     static constexpr auto K1 = Number<K1Value>{};
@@ -179,14 +183,16 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
         const auto N  = b_k0_n_k1_grid_desc.GetLength(I1);
         const auto K0 = a_k0_m_k1_grid_desc.GetLength(I0);
 
-        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
+        static_assert((MPerBlock % (MPerXDL * MRepeat) == 0) &&
+                          (NPerBlock % (NRepeat * NPerXDL)) == 0,
+                      "Invalid tuning param!");
 
+        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
         return (M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
                 K0 == b_k0_n_k1_grid_desc.GetLength(I0) &&
                 K1 == a_k0_m_k1_grid_desc.GetLength(I2) &&
                 K1 == b_k0_n_k1_grid_desc.GetLength(I2)) &&
-               (M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % KPerBlock == 0) &&
-               (MPerBlock % MPerWave == 0 && NPerBlock % NPerWave == 0);
+               (M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % KPerBlock == 0);
     }
 
     __host__ __device__ static constexpr index_t
@@ -201,29 +207,28 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
     }
 
     __host__ __device__ static constexpr auto
-    MakeCM0M1M2NGridDescriptor(const CMNGridDesc& c_m_n_grid_desc)
+    MakeCM0N0M1N1M2M3M4N2GridDescriptor(const CMNGridDesc& c_m_n_grid_desc)
     {
-        constexpr auto xdlops_gemm = XdlopsGemm<FloatAB, MPerWave, NPerWave, K1>{};
+        constexpr auto max_lds_align = K1;
 
-        constexpr auto CLayout = xdlops_gemm.GetCLayout();
+        constexpr auto a_k0_m_k1_block_desc = make_naive_tensor_descriptor_aligned(
+            make_tuple(Number<KPerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
 
-        constexpr auto M0 = Number<CLayout.M1()>{};
-        constexpr auto M1 = Number<CLayout.N1()>{};
-        constexpr auto M2 = Number<CLayout.M0()>{};
+        constexpr auto b_k0_n_k1_block_desc = make_naive_tensor_descriptor_aligned(
+            make_tuple(Number<KPerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
 
-        constexpr index_t MWaves = MPerBlock / (MPerWave * MRepeat);
-        constexpr index_t NWaves = NPerBlock / (NPerWave * NRepeat);
+        using BlockwiseGemm =
+            BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
+                                                                FloatAB,
+                                                                decltype(a_k0_m_k1_block_desc),
+                                                                decltype(b_k0_n_k1_block_desc),
+                                                                MPerXDL,
+                                                                NPerXDL,
+                                                                MRepeat,
+                                                                NRepeat,
+                                                                K1>;
 
-        constexpr auto N1 = Number<CLayout.N0()>{};
-
-        const auto c_m0_m1_m2_n_grid_desc = transform_tensor_descriptor(
-            c_m_n_grid_desc,
-            make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, M0, M1, M2)),
-                       make_unmerge_transform(make_tuple(NRepeat, NWaves, N1))),
-            make_tuple(Sequence<0>{}, Sequence<1>{}),
-            make_tuple(Sequence<0, 2, 4, 5, 6>{}, Sequence<1, 3, 7>{}));
-
-        return c_m0_m1_m2_n_grid_desc;
+        return BlockwiseGemm::MakeCM0N0M1N1M2M3M4N2GridDescriptor(c_m_n_grid_desc);
     }
 
     __host__ __device__ static constexpr auto
@@ -253,8 +258,8 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
         return c_blockid_to_m0_n0_block_cluster_adaptor;
     }
 
-    using CM0M1M2NGridDesc     = decltype(MakeCM0M1M2NGridDescriptor(CMNGridDesc{}));
-    using CBlockClusterAdaptor = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}));
+    using CM0N0M1N1M2M3M4N2GridDesc = decltype(MakeCM0N0M1N1M2M3M4N2GridDescriptor(CMNGridDesc{}));
+    using CBlockClusterAdaptor      = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}));
 
     __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
                                const FloatAB* __restrict__ p_b_grid,
@@ -262,7 +267,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
                                FloatAB* __restrict__ p_shared_block,
                                const AK0MK1GridDesc& a_k0_m_k1_grid_desc,
                                const BK0NK1GridDesc& b_k0_n_k1_grid_desc,
-                               const CM0M1M2NGridDesc& c_m0_m1_m2_n_grid_desc,
+                               const CM0N0M1N1M2M3M4N2GridDesc& c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                                const CBlockClusterAdaptor& c_block_cluster_adaptor)
     {
         const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
@@ -270,7 +275,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
         const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
             p_b_grid, b_k0_n_k1_grid_desc.GetElementSpaceSize());
         auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum_t::Global>(
-            p_c_grid, c_m0_m1_m2_n_grid_desc.GetElementSpaceSize());
+            p_c_grid, c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc.GetElementSpaceSize());
 
         const auto K0 = a_k0_m_k1_grid_desc.GetLength(I0);
 
@@ -358,50 +363,26 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
         //       register
         // sanity check
 
-        static_assert(MPerBlock % (MPerWave * MRepeat) == 0 &&
-                          NPerBlock % (NPerWave * NRepeat) == 0,
-                      "wrong!");
-
-        constexpr auto a_k0_m0_m1_k1_block_desc = transform_tensor_descriptor(
-            a_k0_m_k1_block_desc,
-            make_tuple(make_pass_through_transform(Number<KPerBlock>{}),
-                       make_unmerge_transform(
-                           make_tuple(Number<MRepeat>{}, Number<MPerBlock / MRepeat>{})),
-                       make_pass_through_transform(K1)),
-            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
-            make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
-
-        constexpr auto b_k0_n0_n1_k1_block_desc = transform_tensor_descriptor(
-            b_k0_n_k1_block_desc,
-            make_tuple(make_pass_through_transform(Number<KPerBlock>{}),
-                       make_unmerge_transform(
-                           make_tuple(Number<NRepeat>{}, Number<NPerBlock / NRepeat>{})),
-                       make_pass_through_transform(K1)),
-            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
-            make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
-
         const auto blockwise_gemm =
-            BlockwiseGemmXdlops_km_kn_m0m1m2n_v1<BlockSize,
-                                                 FloatAB,
-                                                 decltype(a_k0_m0_m1_k1_block_desc),
-                                                 decltype(b_k0_n0_n1_k1_block_desc),
-                                                 MPerWave,
-                                                 NPerWave,
-                                                 K1>{};
-
-        constexpr auto CLayout = blockwise_gemm.GetCLayout();
-
-        constexpr index_t BlkSize   = CLayout.GetBlkSize();
-        constexpr index_t NumBlks   = CLayout.GetNumBlks();
-        constexpr index_t NumXdlops = CLayout.GetNumXdlops();
-
-        static_assert(NumBlks == 1 && NumXdlops == 1, "K Reduction Mfma only");
+            BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
+                                                                FloatAB,
+                                                                decltype(a_k0_m_k1_block_desc),
+                                                                decltype(b_k0_n_k1_block_desc),
+                                                                MPerXDL,
+                                                                NPerXDL,
+                                                                MRepeat,
+                                                                NRepeat,
+                                                                K1>{};
 
         constexpr auto c_mr_nr_blk_desc =
             make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{}, Number<NRepeat>{}));
 
+        constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc =
+            blockwise_gemm.GetCM0N0M1N1M2M3M4N2ThreadDescriptor();
+        constexpr auto CBlkSize = c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc.GetElementSpaceSize();
+
         StaticBuffer<AddressSpaceEnum_t::Vgpr,
-                     vector_type<FloatAcc, BlkSize>,
+                     vector_type<FloatAcc, CBlkSize>,
                      c_mr_nr_blk_desc.GetElementSpaceSize(),
                      true>
             c_thread_buf;
@@ -474,41 +455,14 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
             blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
         }
 
-#if 0
         // output: register to global memory
         {
-            constexpr index_t M0 = CLayout.M1();
-            constexpr index_t M1 = CLayout.N1();
-            constexpr index_t M2 = CLayout.M0();
+            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc =
+                blockwise_gemm.GetCM0N0M1N1M2M3M4N2BlockDescriptor();
 
-            constexpr index_t N0 = CLayout.N1();
-            constexpr index_t N1 = CLayout.N0();
-
-            constexpr auto c_m0_m1_m2_n_thread_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
-                                                                          Number<NRepeat>{},
-                                                                          Number<1>{},
-                                                                          Number<1>{},
-                                                                          Number<M0>{},
-                                                                          Number<1>{},
-                                                                          Number<M2>{},
-                                                                          Number<1>{}));
-
-            StaticBuffer<AddressSpaceEnum_t::Vgpr, FloatC, c_m0_m1_m2_n_thread_desc.GetElementSpaceSize(), true>
-                c_blk_buf_;
-
-            static_for<0, MRepeat, 1>{}([&](auto mr_i) {
-                static_for<0, NRepeat, 1>{}([&](auto nr_i) {
-                    constexpr auto blk_off =
-                        c_mr_nr_blk_desc.CalculateOffset(make_tuple(mr_i, nr_i));
-
-                    static_for<0, BlkSize, 1>{}([&](auto j) {
-                        c_blk_buf_(Number<blk_off * BlkSize + j>{}) =
-                            c_thread_buf[Number<blk_off>{}]
-                                .template AsType<FloatAcc>()[Number<j>{}];
-                    });
-                });
-            });
+            constexpr auto M2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I4);
+            constexpr auto M3 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I5);
+            constexpr auto M4 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I6);
 
             // calculate origin of thread output tensor on global memory
             //     blockwise GEMM c matrix starting index
@@ -521,145 +475,96 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
             const index_t n_thread_data_on_grid =
                 n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
 
-            constexpr auto c_m0_m1_m2_n_grid_tensor_step_hacks = CGridStepHacks{};
-
-            constexpr index_t MWaves = MPerBlock / (MPerWave * MRepeat);
-            constexpr index_t NWaves = NPerBlock / (NPerWave * NRepeat);
-
-            ThreadwiseTensorSliceTransfer_v1r3<
-                FloatC,
-                FloatC,
-                decltype(c_m0_m1_m2_n_thread_desc),
-                decltype(c_m0_m1_m2_n_grid_desc),
-                Sequence<MRepeat, NRepeat, 1, 1, M0, 1, M2, 1>,
-                CThreadTransferSrcDstAccessOrder,
-                CThreadTransferSrcDstVectorDim,
-                CThreadTransferDstScalarPerVector,
-                CGlobalMemoryDataOperation,
-                1,
-                true>{
-                c_m0_m1_m2_n_grid_desc,
-                make_multi_index(m_thread_data_on_grid / (M2 * M1 * M0 * MWaves),
-                                 n_thread_data_on_grid / (N1 * NWaves),
-                                 m_thread_data_on_grid % (M2 * M1 * M0 * MWaves) / (M2 * M1 * M0),
-                                 n_thread_data_on_grid % (N1 * NWaves) / N1,
-                                 m_thread_data_on_grid % (M2 * M1 * M0) / (M2 * M1),
-                                 m_thread_data_on_grid % (M2 * M1) / M2,
-                                 m_thread_data_on_grid % M2,
-                                 n_thread_data_on_grid % N1)}
-                .Run(c_m0_m1_m2_n_thread_desc,
-                     make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
-                     c_blk_buf_,
-                     c_m0_m1_m2_n_grid_desc,
-                     c_grid_buf,
-                     c_m0_m1_m2_n_grid_tensor_step_hacks);
-        }
-#else
-        {
-            constexpr index_t M0 = CLayout.M1();
-            constexpr index_t M1 = CLayout.N1();
-            constexpr index_t M2 = CLayout.M0();
-
-            constexpr auto c_m0_m1_m2_n_thread_desc = make_naive_tensor_descriptor_packed(
-                make_tuple(I1, I1, I1, I1, Number<M0>{}, Number<1>{}, Number<M2>{}, Number<1>{}));
-
-            // calculate origin of thread output tensor on global memory
-            //     blockwise GEMM c matrix starting index
-            const auto c_thread_mtx_on_block =
-                blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
-
-            const index_t m_thread_data_on_grid =
-                m_block_data_idx_on_grid + c_thread_mtx_on_block[I0];
-
-            const index_t n_thread_data_on_grid =
-                n_block_data_idx_on_grid + c_thread_mtx_on_block[I1];
-
-            constexpr auto c_m0_m1_m2_n_grid_tensor_step_hacks = CGridStepHacks{};
+            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_tensor_step_hacks = CGridStepHacks{};
 
             auto c_thread_copy =
                 ThreadwiseTensorSliceTransfer_v1r3<FloatC,
                                                    FloatC,
-                                                   decltype(c_m0_m1_m2_n_thread_desc),
-                                                   decltype(c_m0_m1_m2_n_grid_desc),
-                                                   Sequence<1, 1, 1, 1, M0, 1, M2, 1>,
+                                                   decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc),
+                                                   decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc),
+                                                   Sequence<I1, I1, I1, I1, M2, I1, M4, I1>,
                                                    CThreadTransferSrcDstAccessOrder,
                                                    CThreadTransferSrcDstVectorDim,
                                                    CThreadTransferDstScalarPerVector,
                                                    CGlobalMemoryDataOperation,
                                                    1,
                                                    true>{
-                    c_m0_m1_m2_n_grid_desc,
+                    c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                     make_multi_index(0,
                                      0,
                                      0,
                                      0,
-                                     m_thread_data_on_grid / (M2 * M1),
-                                     m_thread_data_on_grid % (M2 * M1) / M2,
-                                     m_thread_data_on_grid % M2,
+                                     m_thread_data_on_grid / (M3 * M4),
+                                     m_thread_data_on_grid % (M3 * M4) / M4,
+                                     m_thread_data_on_grid % M4,
                                      n_thread_data_on_grid)};
 
             auto init_copy = [&](auto c_thread_idx_) {
                 constexpr auto blk_off = c_mr_nr_blk_desc.CalculateOffset(c_thread_idx_);
-                c_thread_copy.Run(c_m0_m1_m2_n_thread_desc,
+                c_thread_copy.Run(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
                                   make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
                                   c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
-                                  c_m0_m1_m2_n_grid_desc,
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                                   c_grid_buf,
-                                  c_m0_m1_m2_n_grid_tensor_step_hacks);
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_tensor_step_hacks);
 
                 return c_thread_idx_;
             };
 
             auto mrepeat_plus_copy = [&](auto c_thread_idx_) {
                 constexpr auto mrepeat_step_plus = make_multi_index(1, 0, 0, 0, 0, 0, 0, 0);
-                c_thread_copy.MoveDstSliceWindow(c_m0_m1_m2_n_grid_desc, mrepeat_step_plus);
+                c_thread_copy.MoveDstSliceWindow(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
+                                                 mrepeat_step_plus);
 
                 constexpr auto blk_off = c_mr_nr_blk_desc.CalculateOffset(c_thread_idx_);
-                c_thread_copy.Run(c_m0_m1_m2_n_thread_desc,
+                c_thread_copy.Run(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
                                   make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
                                   c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
-                                  c_m0_m1_m2_n_grid_desc,
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                                   c_grid_buf,
-                                  c_m0_m1_m2_n_grid_tensor_step_hacks);
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_tensor_step_hacks);
             };
 
             auto nrepeat_plus_copy = [&](auto c_thread_idx_) {
                 constexpr auto nrepeat_step_plus = make_multi_index(0, 1, 0, 0, 0, 0, 0, 0);
-                c_thread_copy.MoveDstSliceWindow(c_m0_m1_m2_n_grid_desc, nrepeat_step_plus);
+                c_thread_copy.MoveDstSliceWindow(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
+                                                 nrepeat_step_plus);
 
                 constexpr auto blk_off = c_mr_nr_blk_desc.CalculateOffset(c_thread_idx_);
-                c_thread_copy.Run(c_m0_m1_m2_n_thread_desc,
+                c_thread_copy.Run(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
                                   make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
                                   c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
-                                  c_m0_m1_m2_n_grid_desc,
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                                   c_grid_buf,
-                                  c_m0_m1_m2_n_grid_tensor_step_hacks);
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_tensor_step_hacks);
             };
 
             auto mrepeat_minus_copy = [&](auto c_thread_idx_) {
                 constexpr auto mrepeat_step_plus = make_multi_index(-1, 0, 0, 0, 0, 0, 0, 0);
-                c_thread_copy.MoveDstSliceWindow(c_m0_m1_m2_n_grid_desc, mrepeat_step_plus);
+                c_thread_copy.MoveDstSliceWindow(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
+                                                 mrepeat_step_plus);
 
                 constexpr auto blk_off = c_mr_nr_blk_desc.CalculateOffset(c_thread_idx_);
-                c_thread_copy.Run(c_m0_m1_m2_n_thread_desc,
+                c_thread_copy.Run(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
                                   make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
                                   c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
-                                  c_m0_m1_m2_n_grid_desc,
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                                   c_grid_buf,
-                                  c_m0_m1_m2_n_grid_tensor_step_hacks);
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_tensor_step_hacks);
             };
 
             auto nrepeat_minus_copy = [&](auto c_thread_idx_) {
                 constexpr auto nrepeat_step_minus = make_multi_index(0, -1, 0, 0, 0, 0, 0, 0);
-                c_thread_copy.MoveDstSliceWindow(c_m0_m1_m2_n_grid_desc, nrepeat_step_minus);
+                c_thread_copy.MoveDstSliceWindow(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
+                                                 nrepeat_step_minus);
 
                 constexpr auto blk_off = c_mr_nr_blk_desc.CalculateOffset(c_thread_idx_);
-                c_thread_copy.Run(c_m0_m1_m2_n_thread_desc,
+                c_thread_copy.Run(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
                                   make_tuple(I0, I0, I0, I0, I0, I0, I0, I0),
                                   c_thread_buf[Number<blk_off>{}].template AsType<FloatAcc>(),
-                                  c_m0_m1_m2_n_grid_desc,
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                                   c_grid_buf,
-                                  c_m0_m1_m2_n_grid_tensor_step_hacks);
+                                  c_m0_n0_m1_n1_m2_m3_m4_n2_grid_tensor_step_hacks);
             };
 
             static_assert((MRepeat == 4 && NRepeat == 4) or (MRepeat == 4 && NRepeat == 2) or
@@ -791,7 +696,6 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
                 init_copy(make_tuple(I0, I0));
             }
         }
-#endif
     }
 }; // namespace ck
 

composable_kernel/include/tensor_operation/threadwise_contraction_dlops.hpp

@@ -55,19 +55,16 @@ struct ThreadwiseGemmDlops_km0m1_kn0n1_m0m1n0n1
                                CBuffer& c_buf,
                                COriginIdx)
     {
-        static_assert(
-            is_known_at_compile_time<remove_cv_t<remove_reference_t<AOriginIdx>>>::value &&
-                is_known_at_compile_time<remove_cv_t<remove_reference_t<BOriginIdx>>>::value &&
-                is_known_at_compile_time<remove_cv_t<remove_reference_t<COriginIdx>>>::value,
-            "wrong! AOriginIdx, BOriginIdx, COringinIdx should be known at compile-time");
+        static_assert(is_known_at_compile_time<remove_cvref_t<AOriginIdx>>::value &&
+                          is_known_at_compile_time<remove_cvref_t<BOriginIdx>>::value &&
+                          is_known_at_compile_time<remove_cvref_t<COriginIdx>>::value,
+                      "wrong! AOriginIdx, BOriginIdx, COringinIdx should be known at compile-time");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename ABuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatA>>>::value &&
-                      is_same<remove_cv_t<remove_reference_t<typename BBuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatB>>>::value &&
-                      is_same<remove_cv_t<remove_reference_t<typename CBuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatC>>>::value &&
-                      "wrong! inconsistent type");
+        static_assert(
+            is_same<remove_cvref_t<typename ABuffer::type>, remove_cvref_t<FloatA>>::value &&
+            is_same<remove_cvref_t<typename BBuffer::type>, remove_cvref_t<FloatB>>::value &&
+            is_same<remove_cvref_t<typename CBuffer::type>, remove_cvref_t<FloatC>>::value &&
+            "wrong! inconsistent type");
 
         constexpr auto I0 = Number<0>{};
         constexpr auto I1 = Number<1>{};
@@ -157,19 +154,16 @@ struct ThreadwiseContractionDlops_A_TK0_TM0_TM1_TK1_B_TK0_TN0_TN1_TK1_C_TM0_TM1_
                                CBuffer& c_buf,
                                COriginIdx)
     {
-        static_assert(
-            is_known_at_compile_time<remove_cv_t<remove_reference_t<AOriginIdx>>>::value &&
-                is_known_at_compile_time<remove_cv_t<remove_reference_t<BOriginIdx>>>::value &&
-                is_known_at_compile_time<remove_cv_t<remove_reference_t<COriginIdx>>>::value,
-            "wrong! AOriginIdx, BOriginIdx, COringinIdx should be known at compile-time");
+        static_assert(is_known_at_compile_time<remove_cvref_t<AOriginIdx>>::value &&
+                          is_known_at_compile_time<remove_cvref_t<BOriginIdx>>::value &&
+                          is_known_at_compile_time<remove_cvref_t<COriginIdx>>::value,
+                      "wrong! AOriginIdx, BOriginIdx, COringinIdx should be known at compile-time");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename ABuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatA>>>::value &&
-                      is_same<remove_cv_t<remove_reference_t<typename BBuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatB>>>::value &&
-                      is_same<remove_cv_t<remove_reference_t<typename CBuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatC>>>::value &&
-                      "wrong! inconsistent type");
+        static_assert(
+            is_same<remove_cvref_t<typename ABuffer::type>, remove_cvref_t<FloatA>>::value &&
+            is_same<remove_cvref_t<typename BBuffer::type>, remove_cvref_t<FloatB>>::value &&
+            is_same<remove_cvref_t<typename CBuffer::type>, remove_cvref_t<FloatC>>::value &&
+            "wrong! inconsistent type");
 
         constexpr auto I0 = Number<0>{};
         constexpr auto I1 = Number<1>{};

composable_kernel/include/tensor_operation/threadwise_gemm_dlops_v3.hpp

@@ -41,19 +41,16 @@ struct ThreadwiseGemmDlops_km_kn_mn_v3
                           CDesc::IsKnownAtCompileTime(),
                       "wrong! Desc should be known at compile-time");
 
-        static_assert(
-            is_known_at_compile_time<remove_cv_t<remove_reference_t<AOriginIdx>>>::value &&
-                is_known_at_compile_time<remove_cv_t<remove_reference_t<BOriginIdx>>>::value &&
-                is_known_at_compile_time<remove_cv_t<remove_reference_t<COriginIdx>>>::value,
-            "wrong! AOriginIdx, BOriginIdx, COringinIdx should be known at compile-time");
+        static_assert(is_known_at_compile_time<remove_cvref_t<AOriginIdx>>::value &&
+                          is_known_at_compile_time<remove_cvref_t<BOriginIdx>>::value &&
+                          is_known_at_compile_time<remove_cvref_t<COriginIdx>>::value,
+                      "wrong! AOriginIdx, BOriginIdx, COringinIdx should be known at compile-time");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename ABuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatA>>>::value &&
-                      is_same<remove_cv_t<remove_reference_t<typename BBuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatB>>>::value &&
-                      is_same<remove_cv_t<remove_reference_t<typename CBuffer::type>>,
-                              remove_cv_t<remove_reference_t<FloatC>>>::value &&
-                      "wrong! inconsistent type");
+        static_assert(
+            is_same<remove_cvref_t<typename ABuffer::type>, remove_cvref_t<FloatA>>::value &&
+            is_same<remove_cvref_t<typename BBuffer::type>, remove_cvref_t<FloatB>>::value &&
+            is_same<remove_cvref_t<typename CBuffer::type>, remove_cvref_t<FloatC>>::value &&
+            "wrong! inconsistent type");
 
         constexpr auto I0 = Number<0>{};
         constexpr auto I1 = Number<1>{};

composable_kernel/include/tensor_operation/threadwise_tensor_slice_set.hpp

@@ -30,11 +30,11 @@ struct ThreadwiseTensorSliceSet_v1
 
         static_assert(Buffer::IsStaticBuffer(), "wrong! DstBuffer need to be StaticBuffer");
 
-        static_assert(is_known_at_compile_time<remove_cv_t<remove_reference_t<OriginIdx>>>::value,
+        static_assert(is_known_at_compile_time<remove_cvref_t<OriginIdx>>::value,
                       "wrong! OriginIdx need to be known at compile-time");
 
         // Desc is known at compile-time
-        constexpr auto desc = remove_cv_t<remove_reference_t<Desc>>{};
+        constexpr auto desc = remove_cvref_t<Desc>{};
 
         // OriginIdx is known at compile-time
         constexpr auto origin_idx = to_multi_index(OriginIdx{});

composable_kernel/include/tensor_operation/threadwise_tensor_slice_transfer.hpp

@@ -95,18 +95,13 @@ struct ThreadwiseTensorSliceTransfer_v1r3
         static_assert(SrcDesc::IsKnownAtCompileTime(),
                       "wrong! SrcDesc need to known at compile-time");
 
-        static_assert(
-            is_known_at_compile_time<remove_cv_t<remove_reference_t<SrcSliceOriginIdx>>>::value,
-            "wrong! SrcSliceOrigin need to known at compile-time");
+        static_assert(is_known_at_compile_time<remove_cvref_t<SrcSliceOriginIdx>>::value,
+                      "wrong! SrcSliceOrigin need to known at compile-time");
 
         static_assert(SrcBuffer::IsStaticBuffer(), "wrong! SrcBuffer need to be StaticBuffer");
 
-        // static_assert(is_same<remove_cv_t<remove_reference_t<typename SrcBuffer::type>>,
-        // remove_cv_t<remove_reference_t<SrcData>>>::value,
-        //"wrong! SrcBuffer data type is wrong");
-
         // SrcDesc and src_slice_origin_idx are known at compile-time
-        constexpr auto src_desc             = remove_cv_t<remove_reference_t<SrcDesc>>{};
+        constexpr auto src_desc             = remove_cvref_t<SrcDesc>{};
         constexpr auto src_slice_origin_idx = to_multi_index(SrcSliceOriginIdx{});
 
         constexpr auto I0 = Number<0>{};
@@ -208,10 +203,20 @@ struct ThreadwiseTensorSliceTransfer_v1r3
                 coordinate_has_valid_offset_assuming_visible_index_is_valid(dst_desc, dst_coord_);
 
             // copy data from dst_vector into dst_buf
-            dst_buf.template Set<dst_vector_t>(
-                dst_coord_.GetOffset(),
-                is_dst_valid,
-                dst_vector.template AsType<dst_vector_t>()[Number<0>{}]);
+            if constexpr(DstInMemOp == InMemoryDataOperationEnum_t::Set)
+            {
+                dst_buf.template Set<dst_vector_t>(
+                    dst_coord_.GetOffset(),
+                    is_dst_valid,
+                    dst_vector.template AsType<dst_vector_t>()[Number<0>{}]);
+            }
+            else if constexpr(DstInMemOp == InMemoryDataOperationEnum_t::AtomicAdd)
+            {
+                dst_buf.template AtomicAdd<dst_vector_t>(
+                    dst_coord_.GetOffset(),
+                    is_dst_valid,
+                    dst_vector.template AsType<dst_vector_t>()[Number<0>{}]);
+            }
 
             constexpr auto move_on_dim = [&]() constexpr
             {
@@ -411,16 +416,15 @@ struct ThreadwiseTensorSliceTransfer_v2
         static_assert(DstDesc::IsKnownAtCompileTime(),
                       "wrong! DstDesc need to known at compile-time");
 
-        static_assert(
-            is_known_at_compile_time<remove_cv_t<remove_reference_t<DstSliceOriginIdx>>>::value,
-            "wrong! DstSliceOrigin need to known at compile-time");
+        static_assert(is_known_at_compile_time<remove_cvref_t<DstSliceOriginIdx>>::value,
+                      "wrong! DstSliceOrigin need to known at compile-time");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename DstBuffer::type>>,
-                              remove_cv_t<remove_reference_t<DstData>>>::value &&
-                      "wrong! inconsistent type");
+        static_assert(
+            is_same<remove_cvref_t<typename DstBuffer::type>, remove_cvref_t<DstData>>::value &&
+            "wrong! inconsistent type");
 
         // DstDesc and dst_slice_origin_idx are known at compile-time
-        constexpr auto dst_desc             = remove_cv_t<remove_reference_t<DstDesc>>{};
+        constexpr auto dst_desc             = remove_cvref_t<DstDesc>{};
         constexpr auto dst_slice_origin_idx = DstSliceOriginIdx{};
 
         constexpr auto I0 = Number<0>{};
@@ -729,9 +733,9 @@ struct ThreadwiseTensorSliceTransfer_v3
                           SrcBuffer::GetAddressSpace() == AddressSpaceEnum_t::Lds,
                       "wrong!");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename SrcBuffer::type>>,
-                              remove_cv_t<remove_reference_t<SrcData>>>::value,
-                      "wrong! SrcBuffer and SrcData data type are inconsistent");
+        static_assert(
+            is_same<remove_cvref_t<typename SrcBuffer::type>, remove_cvref_t<SrcData>>::value,
+            "wrong! SrcBuffer and SrcData data type are inconsistent");
 
         constexpr auto I0 = Number<0>{};
         constexpr auto I1 = Number<1>{};
@@ -886,9 +890,9 @@ struct ThreadwiseTensorSliceTransfer_v3
                           DstBuffer::GetAddressSpace() == AddressSpaceEnum_t::Lds,
                       "wrong!");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename DstBuffer::type>>,
-                              remove_cv_t<remove_reference_t<DstData>>>::value,
-                      "wrong! SrcBuffer or DstBuffer data type is wrong");
+        static_assert(
+            is_same<remove_cvref_t<typename DstBuffer::type>, remove_cvref_t<DstData>>::value,
+            "wrong! SrcBuffer or DstBuffer data type is wrong");
 
         constexpr auto I0 = Number<0>{};
         constexpr auto I1 = Number<1>{};
@@ -1303,24 +1307,21 @@ struct ThreadwiseTensorSliceTransfer_v4
         static_assert(SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
                       "wrong! SrcDesc and DstDesc need to known at compile-time");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename SrcBuffer::type>>,
-                              remove_cv_t<remove_reference_t<SrcData>>>::value &&
-                          is_same<remove_cv_t<remove_reference_t<typename DstBuffer::type>>,
-                                  remove_cv_t<remove_reference_t<DstData>>>::value,
-                      "wrong! SrcBuffer or DstBuffer data type is wrong");
+        static_assert(
+            is_same<remove_cvref_t<typename SrcBuffer::type>, remove_cvref_t<SrcData>>::value &&
+                is_same<remove_cvref_t<typename DstBuffer::type>, remove_cvref_t<DstData>>::value,
+            "wrong! SrcBuffer or DstBuffer data type is wrong");
 
         static_assert(DstBuffer::IsStaticBuffer(), "wrong! DstBuffer need to be StaticBuffer");
 
-        static_assert(
-            is_known_at_compile_time<
-                remove_cv_t<remove_reference_t<SrcRefToOriginDisplacement>>>::value &&
-                is_known_at_compile_time<remove_cv_t<remove_reference_t<DstOriginIdx>>>::value,
-            "wrong! SrcOriginToRefDistance and DstOriginToRefDistance need to be known "
-            "at compile-time");
+        static_assert(is_known_at_compile_time<remove_cvref_t<SrcRefToOriginDisplacement>>::value &&
+                          is_known_at_compile_time<remove_cvref_t<DstOriginIdx>>::value,
+                      "wrong! SrcOriginToRefDistance and DstOriginToRefDistance need to be known "
+                      "at compile-time");
 
         // SrcDesc and DstDesc are known at compile-time
-        constexpr auto src_desc = remove_cv_t<remove_reference_t<SrcDesc>>{};
-        constexpr auto dst_desc = remove_cv_t<remove_reference_t<DstDesc>>{};
+        constexpr auto src_desc = remove_cvref_t<SrcDesc>{};
+        constexpr auto dst_desc = remove_cvref_t<DstDesc>{};
 
         // SrcOriginToRefDisttance and DstOriginToRefDistance are known at compile-time
         constexpr auto src_ref_to_origin_disp_idx = to_multi_index(SrcRefToOriginDisplacement{});

composable_kernel/include/tensor_operation/threadwise_tensor_slice_transfer_v2.hpp

@@ -80,9 +80,9 @@ struct ThreadwiseTensorSliceTransfer_v3r1
                           SrcBuffer::GetAddressSpace() == AddressSpaceEnum_t::Lds,
                       "wrong!");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename SrcBuffer::type>>,
-                              remove_cv_t<remove_reference_t<SrcData>>>::value,
-                      "wrong! SrcBuffer and SrcData data type are inconsistent");
+        static_assert(
+            is_same<remove_cvref_t<typename SrcBuffer::type>, remove_cvref_t<SrcData>>::value,
+            "wrong! SrcBuffer and SrcData data type are inconsistent");
 
         // tensor descriptor for src_vector
         constexpr auto src_vector_tensor_lengths = SrcVectorTensorLengths{};
@@ -248,9 +248,9 @@ struct ThreadwiseTensorSliceTransfer_v3r1
                           DstBuffer::GetAddressSpace() == AddressSpaceEnum_t::Lds,
                       "wrong!");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename DstBuffer::type>>,
-                              remove_cv_t<remove_reference_t<DstData>>>::value,
-                      "wrong! SrcBuffer or DstBuffer data type is wrong");
+        static_assert(
+            is_same<remove_cvref_t<typename DstBuffer::type>, remove_cvref_t<DstData>>::value,
+            "wrong! SrcBuffer or DstBuffer data type is wrong");
 
         // tensor descriptor for dst_vector
         constexpr auto dst_vector_tensor_lengths = DstVectorTensorLengths{};
@@ -669,24 +669,21 @@ struct ThreadwiseTensorSliceTransfer_v4r1
         static_assert(SrcDesc::IsKnownAtCompileTime() && DstDesc::IsKnownAtCompileTime(),
                       "wrong! SrcDesc and DstDesc need to known at compile-time");
 
-        static_assert(is_same<remove_cv_t<remove_reference_t<typename SrcBuffer::type>>,
-                              remove_cv_t<remove_reference_t<SrcData>>>::value &&
-                          is_same<remove_cv_t<remove_reference_t<typename DstBuffer::type>>,
-                                  remove_cv_t<remove_reference_t<DstData>>>::value,
-                      "wrong! SrcBuffer or DstBuffer data type is wrong");
+        static_assert(
+            is_same<remove_cvref_t<typename SrcBuffer::type>, remove_cvref_t<SrcData>>::value &&
+                is_same<remove_cvref_t<typename DstBuffer::type>, remove_cvref_t<DstData>>::value,
+            "wrong! SrcBuffer or DstBuffer data type is wrong");
 
         static_assert(DstBuffer::IsStaticBuffer(), "wrong! DstBuffer need to be StaticBuffer");
 
-        static_assert(
-            is_known_at_compile_time<
-                remove_cv_t<remove_reference_t<SrcRefToOriginDisplacement>>>::value &&
-                is_known_at_compile_time<remove_cv_t<remove_reference_t<DstOriginIdx>>>::value,
-            "wrong! SrcOriginToRefDistance and DstOriginToRefDistance need to be known "
-            "at compile-time");
+        static_assert(is_known_at_compile_time<remove_cvref_t<SrcRefToOriginDisplacement>>::value &&
+                          is_known_at_compile_time<remove_cvref_t<DstOriginIdx>>::value,
+                      "wrong! SrcOriginToRefDistance and DstOriginToRefDistance need to be known "
+                      "at compile-time");
 
         // SrcDesc and DstDesc are known at compile-time
-        constexpr auto src_desc = remove_cv_t<remove_reference_t<SrcDesc>>{};
-        constexpr auto dst_desc = remove_cv_t<remove_reference_t<DstDesc>>{};
+        constexpr auto src_desc = remove_cvref_t<SrcDesc>{};
+        constexpr auto dst_desc = remove_cvref_t<DstDesc>{};
 
         // SrcOriginToRefDisttance and DstOriginToRefDistance are known at compile-time
         constexpr auto src_ref_to_origin_disp_idx = to_multi_index(SrcRefToOriginDisplacement{});

composable_kernel/include/utility/amd_buffer_addressing.hpp

@@ -202,6 +202,22 @@ llvm_amdgcn_raw_buffer_store_fp32x4(float4_t vdata,
                                     index_t voffset,
                                     index_t soffset,
                                     index_t glc_slc) __asm("llvm.amdgcn.raw.buffer.store.v4f32");
+// atomic add
+// int
+__device__ int32_t llvm_amdgcn_raw_buffer_atomic_add_i32(
+    int32_t vdata,
+    int32x4_t rsrc,
+    index_t voffset,
+    index_t soffset,
+    index_t glc_slc) __asm("llvm.amdgcn.raw.buffer.atomic.add.i32");
+
+// float
+__device__ float llvm_amdgcn_raw_buffer_atomic_add_fp32(
+    float vdata,
+    int32x4_t rsrc,
+    index_t voffset,
+    index_t soffset,
+    index_t glc_slc) __asm("llvm.amdgcn.raw.buffer.atomic.fadd.f32");
 
 template <typename T, index_t N>
 __device__ typename vector_type<T, N>::type amd_buffer_load_impl(int32x4_t src_wave_buffer_resource,
@@ -624,8 +640,130 @@ __device__ void amd_buffer_store_impl(const typename vector_type<T, N>::type src
     }
 }
 
+template <typename T, index_t N>
+__device__ void amd_buffer_atomic_add_impl(const typename vector_type<T, N>::type src_thread_data,
+                                           int32x4_t dst_wave_buffer_resource,
+                                           index_t dst_thread_addr_offset,
+                                           index_t dst_wave_addr_offset)
+{
+    static_assert((is_same<T, float>::value && (N == 1 || N == 2 || N == 4)) ||
+                      (is_same<T, int32_t>::value && (N == 1 || N == 2 || N == 4)),
+                  "wrong! not implemented");
+
+    if constexpr(is_same<T, float>::value)
+    {
+        if constexpr(N == 1)
+        {
+            llvm_amdgcn_raw_buffer_atomic_add_fp32(src_thread_data,
+                                                   dst_wave_buffer_resource,
+                                                   dst_thread_addr_offset,
+                                                   dst_wave_addr_offset,
+                                                   0);
+        }
+        else if constexpr(N == 2)
+        {
+            vector_type<float, 2> tmp{src_thread_data};
+
+            llvm_amdgcn_raw_buffer_atomic_add_fp32(tmp.AsType<float>()[Number<0>{}],
+                                                   dst_wave_buffer_resource,
+                                                   dst_thread_addr_offset,
+                                                   dst_wave_addr_offset,
+                                                   0);
+
+            llvm_amdgcn_raw_buffer_atomic_add_fp32(tmp.AsType<float>()[Number<1>{}],
+                                                   dst_wave_buffer_resource,
+                                                   dst_thread_addr_offset,
+                                                   dst_wave_addr_offset + sizeof(float),
+                                                   0);
+        }
+        else if constexpr(N == 4)
+        {
+            vector_type<float, 4> tmp{src_thread_data};
+
+            llvm_amdgcn_raw_buffer_atomic_add_fp32(tmp.AsType<float>()[Number<0>{}],
+                                                   dst_wave_buffer_resource,
+                                                   dst_thread_addr_offset,
+                                                   dst_wave_addr_offset,
+                                                   0);
+
+            llvm_amdgcn_raw_buffer_atomic_add_fp32(tmp.AsType<float>()[Number<1>{}],
+                                                   dst_wave_buffer_resource,
+                                                   dst_thread_addr_offset,
+                                                   dst_wave_addr_offset + sizeof(float),
+                                                   0);
+
+            llvm_amdgcn_raw_buffer_atomic_add_fp32(tmp.AsType<float>()[Number<2>{}],
+                                                   dst_wave_buffer_resource,
+                                                   dst_thread_addr_offset,
+                                                   dst_wave_addr_offset + 2 * sizeof(float),
+                                                   0);
+
+            llvm_amdgcn_raw_buffer_atomic_add_fp32(tmp.AsType<float>()[Number<3>{}],
+                                                   dst_wave_buffer_resource,
+                                                   dst_thread_addr_offset,
+                                                   dst_wave_addr_offset + 3 * sizeof(float),
+                                                   0);
+        }
+    }
+    else if constexpr(is_same<T, int32_t>::value)
+    {
+        if constexpr(N == 1)
+        {
+            llvm_amdgcn_raw_buffer_atomic_add_i32(src_thread_data,
+                                                  dst_wave_buffer_resource,
+                                                  dst_thread_addr_offset,
+                                                  dst_wave_addr_offset,
+                                                  0);
+        }
+        else if constexpr(N == 2)
+        {
+            vector_type<int32_t, 2> tmp{src_thread_data};
+
+            llvm_amdgcn_raw_buffer_atomic_add_i32(tmp.AsType<int32_t>()[Number<0>{}],
+                                                  dst_wave_buffer_resource,
+                                                  dst_thread_addr_offset,
+                                                  dst_wave_addr_offset,
+                                                  0);
+
+            llvm_amdgcn_raw_buffer_atomic_add_i32(tmp.AsType<int32_t>()[Number<1>{}],
+                                                  dst_wave_buffer_resource,
+                                                  dst_thread_addr_offset,
+                                                  dst_wave_addr_offset + sizeof(int32_t),
+                                                  0);
+        }
+        else if constexpr(N == 4)
+        {
+            vector_type<int32_t, 4> tmp{src_thread_data};
+
+            llvm_amdgcn_raw_buffer_atomic_add_i32(tmp.AsType<int32_t>()[Number<0>{}],
+                                                  dst_wave_buffer_resource,
+                                                  dst_thread_addr_offset,
+                                                  dst_wave_addr_offset,
+                                                  0);
+
+            llvm_amdgcn_raw_buffer_atomic_add_i32(tmp.AsType<int32_t>()[Number<1>{}],
+                                                  dst_wave_buffer_resource,
+                                                  dst_thread_addr_offset,
+                                                  dst_wave_addr_offset + sizeof(int32_t),
+                                                  0);
+
+            llvm_amdgcn_raw_buffer_atomic_add_i32(tmp.AsType<int32_t>()[Number<2>{}],
+                                                  dst_wave_buffer_resource,
+                                                  dst_thread_addr_offset,
+                                                  dst_wave_addr_offset + 2 * sizeof(int32_t),
+                                                  0);
+
+            llvm_amdgcn_raw_buffer_atomic_add_i32(tmp.AsType<int32_t>()[Number<3>{}],
+                                                  dst_wave_buffer_resource,
+                                                  dst_thread_addr_offset,
+                                                  dst_wave_addr_offset + 3 * sizeof(int32_t),
+                                                  0);
+        }
+    }
+}
+
 // buffer_load requires:
-//   1) p_src_wave must be in global memory space
+//   1) p_src_wave must point to global memory space
 //   2) p_src_wave must be a wavewise pointer.
 // It is user's responsibility to make sure that is true.
 template <typename T, index_t N>
@@ -659,7 +797,7 @@ amd_buffer_load_invalid_element_return_return_zero(const T* p_src_wave,
 }
 
 // buffer_load requires:
-//   1) p_src_wave must be in global memory space
+//   1) p_src_wave must point to global memory space
 //   2) p_src_wave must be a wavewise pointer.
 // It is user's responsibility to make sure that is true.
 template <typename T, index_t N>
@@ -687,8 +825,8 @@ amd_buffer_load_invalid_element_return_customized_value(const T* p_src_wave,
 }
 
 // buffer_store requires:
-//   1) p_dst_wave must be global memory
-//   2) p_dst_wave to be a wavewise pointer.
+//   1) p_dst_wave must point to global memory
+//   2) p_dst_wave must be a wavewise pointer.
 // It is user's responsibility to make sure that is true.
 template <typename T, index_t N>
 __device__ void amd_buffer_store(const typename vector_type_maker<T, N>::type::type src_thread_data,
@@ -720,5 +858,40 @@ __device__ void amd_buffer_store(const typename vector_type_maker<T, N>::type::t
 #endif
 }
 
+// buffer_atomic_add requires:
+//   1) p_dst_wave must point to global memory
+//   2) p_dst_wave must be a wavewise pointer.
+// It is user's responsibility to make sure that is true.
+template <typename T, index_t N>
+__device__ void
+amd_buffer_atomic_add(const typename vector_type_maker<T, N>::type::type src_thread_data,
+                      T* p_dst_wave,
+                      const index_t dst_thread_element_offset,
+                      const bool dst_thread_element_valid,
+                      const index_t dst_element_space_size)
+{
+    const int32x4_t dst_wave_buffer_resource =
+        make_wave_buffer_resource(p_dst_wave, dst_element_space_size);
+
+    index_t dst_thread_addr_offset = dst_thread_element_offset * sizeof(T);
+
+    using vector_t                = typename vector_type_maker<T, N>::type::type;
+    using scalar_t                = typename scalar_type<vector_t>::type;
+    constexpr index_t vector_size = scalar_type<vector_t>::vector_size;
+
+#if CK_EXPERIMENTAL_USE_BUFFER_ATOMIC_ADD_OOB_CHECK_OFFSET_TRICK
+    uint32_t dst_addr_shift = dst_thread_element_valid ? 0 : 0x7fffffff;
+
+    amd_buffer_atomic_add_impl<scalar_t, vector_size>(
+        src_thread_data, dst_wave_buffer_resource, dst_addr_shift + dst_thread_addr_offset, 0);
+#else
+    if(dst_thread_element_valid)
+    {
+        amd_buffer_atomic_add_impl<scalar_t, vector_size>(
+            src_thread_data, dst_wave_buffer_resource, dst_thread_addr_offset, 0);
+    }
+#endif
+}
+
 } // namespace ck
 #endif

composable_kernel/include/utility/array.hpp

@@ -48,7 +48,7 @@ struct Array<TData, 0>
 template <typename X, typename... Xs>
 __host__ __device__ constexpr auto make_array(X&& x, Xs&&... xs)
 {
-    using data_type = remove_cv_t<remove_reference_t<X>>;
+    using data_type = remove_cvref_t<X>;
     return Array<data_type, sizeof...(Xs) + 1>{{std::forward<X>(x), std::forward<Xs>(xs)...}};
 }
 

composable_kernel/include/utility/config.hpp

@@ -85,8 +85,8 @@
 #define CK_EXPERIMENTAL_USE_BUFFER_STORE_OOB_CHECK_OFFSET_TRICK 1
 #endif
 
-#ifndef CK_EXPERIMENTAL_USE_BUFFER_ATOMIC_OOB_CHECK_OFFSET_TRICK
-#define CK_EXPERIMENTAL_USE_BUFFER_ATOMIC_OOB_CHECK_OFFSET_TRICK 1
+#ifndef CK_EXPERIMENTAL_USE_BUFFER_ATOMIC_ADD_OOB_CHECK_OFFSET_TRICK
+#define CK_EXPERIMENTAL_USE_BUFFER_ATOMIC_ADD_OOB_CHECK_OFFSET_TRICK 1
 #endif
 
 // pass tensor descriptor by value or void*

composable_kernel/include/utility/dynamic_buffer.hpp

@@ -43,18 +43,15 @@ struct DynamicBuffer
     __host__ __device__ constexpr T& operator()(index_t i) { return p_data_[i]; }
 
     template <typename X,
-              typename enable_if<
-                  is_same<typename scalar_type<remove_cv_t<remove_reference_t<X>>>::type,
-                          typename scalar_type<remove_cv_t<remove_reference_t<T>>>::type>::value,
-                  bool>::type = false>
+              typename enable_if<is_same<typename scalar_type<remove_cvref_t<X>>::type,
+                                         typename scalar_type<remove_cvref_t<T>>::type>::value,
+                                 bool>::type = false>
     __host__ __device__ constexpr auto Get(index_t i, bool is_valid_element) const
     {
         // X contains multiple T
-        constexpr index_t scalar_per_t_vector =
-            scalar_type<remove_cv_t<remove_reference_t<T>>>::vector_size;
+        constexpr index_t scalar_per_t_vector = scalar_type<remove_cvref_t<T>>::vector_size;
 
-        constexpr index_t scalar_per_x_vector =
-            scalar_type<remove_cv_t<remove_reference_t<X>>>::vector_size;
+        constexpr index_t scalar_per_x_vector = scalar_type<remove_cvref_t<X>>::vector_size;
 
         static_assert(scalar_per_x_vector % scalar_per_t_vector == 0,
                       "wrong! X need to be multiple T");
@@ -71,15 +68,14 @@ struct DynamicBuffer
 
             if constexpr(InvalidElementUseNumericalZeroValue)
             {
-                return amd_buffer_load_invalid_element_return_return_zero<
-                    remove_cv_t<remove_reference_t<T>>,
-                    t_per_x>(p_data_, i, is_valid_element, element_space_size_);
+                return amd_buffer_load_invalid_element_return_return_zero<remove_cvref_t<T>,
+                                                                          t_per_x>(
+                    p_data_, i, is_valid_element, element_space_size_);
             }
             else
             {
-                return amd_buffer_load_invalid_element_return_customized_value<
-                    remove_cv_t<remove_reference_t<T>>,
-                    t_per_x>(
+                return amd_buffer_load_invalid_element_return_customized_value<remove_cvref_t<T>,
+                                                                               t_per_x>(
                     p_data_, i, is_valid_element, element_space_size_, invalid_element_value_);
             }
         }
@@ -98,18 +94,15 @@ struct DynamicBuffer
     }
 
     template <typename X,
-              typename enable_if<
-                  is_same<typename scalar_type<remove_cv_t<remove_reference_t<X>>>::type,
-                          typename scalar_type<remove_cv_t<remove_reference_t<T>>>::type>::value,
-                  bool>::type = false>
+              typename enable_if<is_same<typename scalar_type<remove_cvref_t<X>>::type,
+                                         typename scalar_type<remove_cvref_t<T>>::type>::value,
+                                 bool>::type = false>
     __host__ __device__ void Set(index_t i, bool is_valid_element, const X& x)
     {
         // X contains multiple T
-        constexpr index_t scalar_per_t_vector =
-            scalar_type<remove_cv_t<remove_reference_t<T>>>::vector_size;
+        constexpr index_t scalar_per_t_vector = scalar_type<remove_cvref_t<T>>::vector_size;
 
-        constexpr index_t scalar_per_x_vector =
-            scalar_type<remove_cv_t<remove_reference_t<X>>>::vector_size;
+        constexpr index_t scalar_per_x_vector = scalar_type<remove_cvref_t<X>>::vector_size;
 
         static_assert(scalar_per_x_vector % scalar_per_t_vector == 0,
                       "wrong! X need to be multiple T");
@@ -119,7 +112,7 @@ struct DynamicBuffer
 #if CK_USE_AMD_BUFFER_ADDRESSING
             constexpr index_t t_per_x = scalar_per_x_vector / scalar_per_t_vector;
 
-            amd_buffer_store<remove_cv_t<remove_reference_t<T>>, t_per_x>(
+            amd_buffer_store<remove_cvref_t<T>, t_per_x>(
                 x, p_data_, i, is_valid_element, element_space_size_);
 #else
             if(is_valid_element)
@@ -140,70 +133,65 @@ struct DynamicBuffer
                 // ISA, so I try to let compiler emit IR "store<i32, 4>" which would be lower to
                 // ds_write_b128
                 // TODO: remove this after compiler fix
-                if constexpr(is_same<typename scalar_type<remove_cv_t<remove_reference_t<T>>>::type,
-                                     int8_t>::value)
+                if constexpr(is_same<typename scalar_type<remove_cvref_t<T>>::type, int8_t>::value)
                 {
-                    static_assert(
-                        (is_same<remove_cv_t<remove_reference_t<T>>, int8_t>::value &&
-                         is_same<remove_cv_t<remove_reference_t<X>>, int8_t>::value) ||
-                            (is_same<remove_cv_t<remove_reference_t<T>>, int8_t>::value &&
-                             is_same<remove_cv_t<remove_reference_t<X>>, int8x2_t>::value) ||
-                            (is_same<remove_cv_t<remove_reference_t<T>>, int8_t>::value &&
-                             is_same<remove_cv_t<remove_reference_t<X>>, int8x4_t>::value) ||
-                            (is_same<remove_cv_t<remove_reference_t<T>>, int8x4_t>::value &&
-                             is_same<remove_cv_t<remove_reference_t<X>>, int8x4_t>::value) ||
-                            (is_same<remove_cv_t<remove_reference_t<T>>, int8x8_t>::value &&
-                             is_same<remove_cv_t<remove_reference_t<X>>, int8x8_t>::value) ||
-                            (is_same<remove_cv_t<remove_reference_t<T>>, int8x16_t>::value &&
-                             is_same<remove_cv_t<remove_reference_t<X>>, int8x16_t>::value),
-                        "wrong! not implemented for this combination, please add "
-                        "implementation");
+                    static_assert((is_same<remove_cvref_t<T>, int8_t>::value &&
+                                   is_same<remove_cvref_t<X>, int8_t>::value) ||
+                                      (is_same<remove_cvref_t<T>, int8_t>::value &&
+                                       is_same<remove_cvref_t<X>, int8x2_t>::value) ||
+                                      (is_same<remove_cvref_t<T>, int8_t>::value &&
+                                       is_same<remove_cvref_t<X>, int8x4_t>::value) ||
+                                      (is_same<remove_cvref_t<T>, int8x4_t>::value &&
+                                       is_same<remove_cvref_t<X>, int8x4_t>::value) ||
+                                      (is_same<remove_cvref_t<T>, int8x8_t>::value &&
+                                       is_same<remove_cvref_t<X>, int8x8_t>::value) ||
+                                      (is_same<remove_cvref_t<T>, int8x16_t>::value &&
+                                       is_same<remove_cvref_t<X>, int8x16_t>::value),
+                                  "wrong! not implemented for this combination, please add "
+                                  "implementation");
 
-                    if constexpr(is_same<remove_cv_t<remove_reference_t<T>>, int8_t>::value &&
-                                 is_same<remove_cv_t<remove_reference_t<X>>, int8_t>::value)
+                    if constexpr(is_same<remove_cvref_t<T>, int8_t>::value &&
+                                 is_same<remove_cvref_t<X>, int8_t>::value)
                     {
                         // HACK: cast pointer of x is bad
                         // TODO: remove this after compiler fix
                         *c_style_pointer_cast<int8_t*>(&p_data_[i]) =
                             *c_style_pointer_cast<const int8_t*>(&x);
                     }
-                    else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>, int8_t>::value &&
-                                      is_same<remove_cv_t<remove_reference_t<X>>, int8x2_t>::value)
+                    else if constexpr(is_same<remove_cvref_t<T>, int8_t>::value &&
+                                      is_same<remove_cvref_t<X>, int8x2_t>::value)
                     {
                         // HACK: cast pointer of x is bad
                         // TODO: remove this after compiler fix
                         *c_style_pointer_cast<int16_t*>(&p_data_[i]) =
                             *c_style_pointer_cast<const int16_t*>(&x);
                     }
-                    else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>, int8_t>::value &&
-                                      is_same<remove_cv_t<remove_reference_t<X>>, int8x4_t>::value)
+                    else if constexpr(is_same<remove_cvref_t<T>, int8_t>::value &&
+                                      is_same<remove_cvref_t<X>, int8x4_t>::value)
                     {
                         // HACK: cast pointer of x is bad
                         // TODO: remove this after compiler fix
                         *c_style_pointer_cast<int32_t*>(&p_data_[i]) =
                             *c_style_pointer_cast<const int32_t*>(&x);
                     }
-                    else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>,
-                                              int8x4_t>::value &&
-                                      is_same<remove_cv_t<remove_reference_t<X>>, int8x4_t>::value)
+                    else if constexpr(is_same<remove_cvref_t<T>, int8x4_t>::value &&
+                                      is_same<remove_cvref_t<X>, int8x4_t>::value)
                     {
                         // HACK: cast pointer of x is bad
                         // TODO: remove this after compiler fix
                         *c_style_pointer_cast<int32_t*>(&p_data_[i]) =
                             *c_style_pointer_cast<const int32_t*>(&x);
                     }
-                    else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>,
-                                              int8x8_t>::value &&
-                                      is_same<remove_cv_t<remove_reference_t<X>>, int8x8_t>::value)
+                    else if constexpr(is_same<remove_cvref_t<T>, int8x8_t>::value &&
+                                      is_same<remove_cvref_t<X>, int8x8_t>::value)
                     {
                         // HACK: cast pointer of x is bad
                         // TODO: remove this after compiler fix
                         *c_style_pointer_cast<int32x2_t*>(&p_data_[i]) =
                             *c_style_pointer_cast<const int32x2_t*>(&x);
                     }
-                    else if constexpr(is_same<remove_cv_t<remove_reference_t<T>>,
-                                              int8x16_t>::value &&
-                                      is_same<remove_cv_t<remove_reference_t<X>>, int8x16_t>::value)
+                    else if constexpr(is_same<remove_cvref_t<T>, int8x16_t>::value &&
+                                      is_same<remove_cvref_t<X>, int8x16_t>::value)
                     {
                         // HACK: cast pointer of x is bad
                         // TODO: remove this after compiler fix
@@ -227,6 +215,35 @@ struct DynamicBuffer
         }
     }
 
+    template <typename X,
+              typename enable_if<is_same<typename scalar_type<remove_cvref_t<X>>::type,
+                                         typename scalar_type<remove_cvref_t<T>>::type>::value,
+                                 bool>::type = false>
+    __host__ __device__ void AtomicAdd(index_t i, bool is_valid_element, const X& x)
+    {
+        // X contains multiple T
+        constexpr index_t scalar_per_t_vector = scalar_type<remove_cvref_t<T>>::vector_size;
+
+        constexpr index_t scalar_per_x_vector = scalar_type<remove_cvref_t<X>>::vector_size;
+
+        static_assert(scalar_per_x_vector % scalar_per_t_vector == 0,
+                      "wrong! X need to be multiple T");
+
+        static_assert(GetAddressSpace() == AddressSpaceEnum_t::Global, "only support global mem");
+
+#if CK_USE_AMD_BUFFER_ADDRESSING
+        constexpr index_t t_per_x = scalar_per_x_vector / scalar_per_t_vector;
+
+        amd_buffer_atomic_add<remove_cvref_t<T>, t_per_x>(
+            x, p_data_, i, is_valid_element, element_space_size_);
+#else
+        if(is_valid_element)
+        {
+            atomicAdd(&p_data_[i], x);
+        }
+#endif
+    }
+
     __host__ __device__ static constexpr bool IsStaticBuffer() { return false; }
 
     __host__ __device__ static constexpr bool IsDynamicBuffer() { return true; }

composable_kernel/include/utility/magic_division.hpp

@@ -114,12 +114,11 @@ struct MagicDivision
     __host__ __device__ static constexpr uint32_t
     DoMagicDivision(uint32_t dividend, uint32_t multiplier, uint32_t shift)
     {
-        uint32_t tmp = (uint64_t(dividend) * uint64_t(multiplier)) >> 32;
+        uint32_t tmp = __umulhi(dividend, multiplier);
         return (tmp + dividend) >> shift;
     }
 
-#if 1 // debug
-    // HACK: magic division for int32_t
+    // magic division for int32_t
     // HACK: use dividend_i32 as if it's uint32_t, dividend_i32 need to be
     // non-negative for result to be correct
     // TODO: figure out how to do magic number divison for int32_t as dividended
@@ -127,27 +126,9 @@ struct MagicDivision
     DoMagicDivision(int32_t dividend_i32, uint32_t multiplier, uint32_t shift)
     {
         uint32_t dividend_u32 = as_type<uint32_t>(dividend_i32);
-        uint32_t tmp =
-            (static_cast<uint64_t>(dividend_u32) * static_cast<uint64_t>(multiplier)) >> 32;
+        uint32_t tmp          = __umulhi(dividend_u32, multiplier);
         return (tmp + dividend_u32) >> shift;
     }
-#else
-    // the inline ASM is producing wrong result
-    __host__ __device__ static int32_t
-    DoMagicDivision(int32_t dividend_i32, uint32_t multiplier, uint32_t shift)
-    {
-        uint32_t r;
-        asm volatile("\n \
-                v_mul_hi_u32 %0, %1, %2 \n \
-                v_add_u32_e32 %0, %1, %0 \n \
-                v_lshrrev_b32_e32 %0, %3, %0 \n \
-                "
-                     : "=v"(r)
-                     : "v"(as_type<uint32_t>(dividend_i32)), "s"(multiplier), "s"(shift));
-
-        return as_type<int32_t>(r);
-    }
-#endif
 };
 
 } // namespace ck

composable_kernel/include/utility/tuple.hpp

@@ -159,7 +159,7 @@ struct Tuple : detail::TupleImpl<typename arithmetic_sequence_gen<0, sizeof...(X
 template <typename... Xs>
 __host__ __device__ constexpr auto make_tuple(Xs&&... xs)
 {
-    return Tuple<remove_cv_t<remove_reference_t<Xs>>...>(std::forward<Xs>(xs)...);
+    return Tuple<remove_cvref_t<Xs>...>(std::forward<Xs>(xs)...);
 }
 
 } // namespace ck

composable_kernel/include/utility/tuple_helper.hpp

@@ -14,9 +14,7 @@ struct is_known_at_compile_time<Tuple<Ts...>>
         return container_reduce(
             Tuple<Ts...>{},
             [](auto x, bool r) {
-                return is_known_at_compile_time<
-                           remove_cv_t<remove_reference_t<decltype(x)>>>::value &
-                       r;
+                return is_known_at_compile_time<remove_cvref_t<decltype(x)>>::value & r;
             },
             true);
     }

composable_kernel/src/kernel_wrapper/convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.cpp

@@ -374,13 +374,8 @@ extern "C" __global__ void
                                           CGridDesc_GM10_BM0_BM1_GN10_BN0_BN1{},
                                           CGridBlockCluster_BlockId_To_GM10_GN10{}));
 
-    const auto desc_tuple = *reinterpret_cast<const DescTuple*>(
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wold-style-cast"
-        // TODO: how to cast?
-        (const void*)p_desc_tuple
-#pragma clang diagnostic pop
-    );
+    const auto desc_tuple =
+        *reinterpret_cast<const DescTuple*>(cast_pointer_to_generic_address_space(p_desc_tuple));
 
     const auto a_grid_desc_gk0_gm0_gm10_gm11_gk1         = desc_tuple[I0];
     const auto b_grid_desc_gk0_gn0_gn10_gn11_gk1         = desc_tuple[I1];

host/driver_offline/CMakeLists.txt

@@ -13,9 +13,15 @@ include_directories(BEFORE
 
 set(CONV_FWD_DRIVER_OFFLINE_SOURCE src/conv_fwd_driver_offline.cpp)
 set(CONV_BWD_DRIVER_OFFLINE_SOURCE src/conv_bwd_driver_offline.cpp)
+set(CONV_WRW_DRIVER_OFFLINE_SOURCE src/conv_wrw_driver_offline.cpp)
+set(GEMM_DRIVER_OFFLINE_SOURCE src/gemm_driver_offline.cpp)
 
 add_executable(conv_fwd_driver_offline ${CONV_FWD_DRIVER_OFFLINE_SOURCE})
 add_executable(conv_bwd_driver_offline ${CONV_BWD_DRIVER_OFFLINE_SOURCE})
+add_executable(conv_wrw_driver_offline ${CONV_WRW_DRIVER_OFFLINE_SOURCE})
+add_executable(gemm_driver_offline ${GEMM_DRIVER_OFFLINE_SOURCE})
 
 target_link_libraries(conv_fwd_driver_offline PRIVATE host_tensor)
 target_link_libraries(conv_bwd_driver_offline PRIVATE host_tensor)
+target_link_libraries(conv_wrw_driver_offline PRIVATE host_tensor)
+target_link_libraries(gemm_driver_offline PRIVATE host_tensor)

host/driver_offline/include/device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk.hpp

@@ -56,9 +56,9 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 128;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 4;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 4;
 
     constexpr index_t MRepeat = 2;
     constexpr index_t NRepeat = 2;
@@ -84,9 +84,9 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 128;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 8;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 8;
 
     constexpr index_t MRepeat = 2;
     constexpr index_t NRepeat = 2;
@@ -112,9 +112,9 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 128;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 8;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 8;
 
     constexpr index_t MRepeat = 4;
     constexpr index_t NRepeat = 2;
@@ -140,9 +140,9 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 256;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 8;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 8;
 
     constexpr index_t MRepeat = 2;
     constexpr index_t NRepeat = 4;
@@ -168,9 +168,9 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 128;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 4;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 4;
 
     constexpr index_t MRepeat = 4;
     constexpr index_t NRepeat = 2;
@@ -208,40 +208,42 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
 
     // HACK: hacks that control index calculation when iterating over A, B, C matrix
     constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks =
-        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},   // 0+: gemmk0
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: gemmm
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 2+: gemmk1
-                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},   // 0-: Gemmk0
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: Gemmm
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: Gemmk1
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},   // 0+: GemmK0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: GemmM
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 2+: GemmK1
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},   // 0-: GemmK0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: GemmM
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: GemmK1
 
     constexpr auto out_gemmk0_gemmn_gemmk1_grid_step_hacks = make_tuple(
-        make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},   // 0+: gemmk0
-                   Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0>{},   // 1+: gemmn
-                   Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 2+: gemmk1
-        make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},   // 0-: gemmk0
-                   Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0>{},   // 1-: gemmn
-                   Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: gemmk1
+        make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},   // 0+: GemmK0
+                   Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0>{},   // 1+: GemmN
+                   Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 2+: GemmK1
+        make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},   // 0-: GemmK0
+                   Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0>{},   // 1-: GemmN
+                   Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: GemmK1
 
-    constexpr auto in_m0_m1_m2_n_grid_step_hacks = make_tuple(
+    // clang-format off
+    constexpr auto in_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks = make_tuple(
         make_tuple(
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},  // 0+: MRepeat
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},  // 1+: NRepeat
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},  // 2+: MWaves
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},  // 3+: NWaves
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},  // 4+: M0
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},  // 5+: M1
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},  // 6+: M2
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}), // 7+: N1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0+: M0
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: N0
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2+: M1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3+: N1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4+: M2
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5+: M3
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6+: M4
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 7+: N2
         make_tuple(
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: MRepeat
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},   // 1-: NRepeat
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: MWaves
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},   // 3-: NWaves
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M0
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M1
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M2
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{})); // 7-: N1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: M0
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: N0
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: M1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: N1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M2
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M3
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M4
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
+    //clang-format on
 
     constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
         Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{};
@@ -263,8 +265,8 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
             GemmMPerBlock,
             GemmNPerBlock,
             GemmKPerBlock,
-            GemmMPerWave,
-            GemmNPerWave,
+            GemmMPerXDL,
+            GemmNPerXDL,
             GemmK1,
             MRepeat,
             NRepeat,
@@ -289,7 +291,7 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
             GemmCThreadTransferDstScalarPerVector,
             decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks),
             decltype(out_gemmk0_gemmn_gemmk1_grid_step_hacks),
-            decltype(in_m0_m1_m2_n_grid_step_hacks),
+            decltype(in_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
             decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
             decltype(out_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
             false // CAccessOrderMRepeatNRepeat
@@ -301,7 +303,7 @@ void device_convolution_backward_data_implicit_gemm_v4r1_xdlops_nhwc_kyxc_nhwk(
               in_gemmm_gemmn_grid_desc,
               wei_gemmk0_gemmm_gemmk1_grid_step_hacks,
               out_gemmk0_gemmn_gemmk1_grid_step_hacks,
-              in_m0_m1_m2_n_grid_step_hacks,
+              in_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
               wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
               out_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
               nrepeat);

host/driver_offline/include/device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk.hpp

@@ -195,25 +195,27 @@ void device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk
                               Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: Gemmn
                               Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 2-: Gemmk1
 
-    constexpr auto in_m0_m1_m2_n_grid_step_hacks = make_tuple(
+    // clang-format off
+    constexpr auto in_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks = make_tuple(
         make_tuple(
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},  // 0+: MRepeat
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},  // 1+: NRepeat
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},  // 2+: MWaves
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},  // 3+: NWaves
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},  // 4+: M0
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},  // 5+: M1
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},  // 6+: M2
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}), // 7+: N1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0+: M0
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: N0
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2+: M1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3+: N1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4+: M2
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5+: M3
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6+: M4
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 7+: N2
         make_tuple(
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},   // 0-: MRepeat
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: NRepeat
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},   // 2-: MWaves
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: NWaves
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},   // 4-: M0
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},   // 5-: M1
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},   // 6-: M2
-            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: M0
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: N0
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: M1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: N1
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M2
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M3
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M4
+            Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
+    //clang-format on
 
     constexpr auto out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
         Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0>{};
@@ -265,7 +267,7 @@ void device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk
             GemmCThreadTransferDstScalarPerVector,
             decltype(out_gemmk0_gemmm_gemmk1_grid_step_hacks),
             decltype(wei_gemmk0_gemmn_gemmk1_grid_step_hacks),
-            decltype(in_m0_m1_m2_n_grid_step_hacks),
+            decltype(in_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
             decltype(out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
             decltype(wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
             true // CAccessOrderMRepeatNRepeat
@@ -277,7 +279,7 @@ void device_convolution_backward_data_implicit_gemm_v4r1r2_xdlops_nhwc_kyxc_nhwk
               in_gemmm_gemmn_grid_desc,
               out_gemmk0_gemmm_gemmk1_grid_step_hacks,
               wei_gemmk0_gemmn_gemmk1_grid_step_hacks,
-              in_m0_m1_m2_n_grid_step_hacks,
+              in_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
               out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
               wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
               nrepeat);

host/driver_offline/include/device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp

@@ -0,0 +1,228 @@
+#include <unistd.h>
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "transform_backward_weight_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw.hpp"
+#include "driver_gemm_xdlops_v2r3.hpp"
+
+template <typename TInWei,
+          typename TAcc,
+          typename TOut,
+          typename InLengths,
+          typename WeiLengths,
+          typename OutLengths,
+          typename ConvStrides,
+          typename ConvDilations,
+          typename InLeftPads,
+          typename InRightPads>
+void device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
+    const InLengths& in_n_c_hi_wi_lengths,
+    const WeiLengths& wei_k_c_y_x_lengths,
+    const OutLengths& out_n_k_ho_wo_lengths,
+    const ConvStrides& conv_strides,
+    const ConvDilations& conv_dilations,
+    const InLeftPads& in_left_pads,
+    const InRightPads& in_right_pads,
+    const Tensor<TInWei>& in_n_c_hi_wi,
+    Tensor<TInWei>& wei_k_c_y_x,
+    const Tensor<TOut>& out_n_k_ho_wo,
+    ck::index_t nrepeat)
+{
+    using namespace ck;
+
+    std::cout << __func__ << std::endl;
+
+    constexpr auto I0 = Number<0>{};
+    constexpr auto I1 = Number<1>{};
+    constexpr auto I2 = Number<2>{};
+
+    DeviceMem in_n_c_hi_wi_device_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
+    DeviceMem wei_k_c_y_x_device_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
+    DeviceMem out_n_k_ho_wo_device_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
+
+    in_n_c_hi_wi_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
+    wei_k_c_y_x_device_buf.ToDevice(wei_k_c_y_x.mData.data());
+    out_n_k_ho_wo_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
+
+    const auto in_n_c_hi_wi_desc  = make_naive_tensor_descriptor_packed(in_n_c_hi_wi_lengths);
+    const auto wei_k_c_y_x_desc   = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
+    const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
+
+#if 1
+    // [M, N, K0, K1] = [128, 128, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t GemmMPerBlock = 128;
+    constexpr index_t GemmNPerBlock = 128;
+    constexpr index_t GemmKPerBlock = 4;
+
+    constexpr index_t GemmMPerWave = 32;
+    constexpr index_t GemmNPerWave = 32;
+    constexpr index_t GemmK1       = 8;
+
+    constexpr index_t MRepeat = 2;
+    constexpr index_t NRepeat = 2;
+
+    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 2, 8>;
+    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
+    // using vector load 4, so config's wo*ho  must be a multiple of 4
+    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
+    constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
+
+    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 8>;
+    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
+
+    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN  = 1;
+    constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
+
+    constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
+#elif 1
+    // [M, N, K0, K1] = [128, 128, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t GemmMPerBlock = 256;
+    constexpr index_t GemmNPerBlock = 128;
+    constexpr index_t GemmKPerBlock = 4;
+
+    constexpr index_t GemmMPerWave = 32;
+    constexpr index_t GemmNPerWave = 32;
+    constexpr index_t GemmK1       = 8;
+
+    constexpr index_t MRepeat = 4;
+    constexpr index_t NRepeat = 2;
+
+    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 4, 8>;
+    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
+    // using vector load 4, so config's wo*ho  must be a multiple of 4
+    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
+    constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
+
+    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 8>;
+    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
+
+    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN  = 1;
+    constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
+
+    constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
+#endif
+
+    const auto descs = transform_backward_weight_convolution_into_gemm_v4r4r2_nchw_kcyx_nkhw_pad(
+        wei_k_c_y_x_desc,
+        in_n_c_hi_wi_desc,
+        out_n_k_ho_wo_desc,
+        conv_strides,
+        conv_dilations,
+        in_left_pads,
+        in_right_pads,
+        Number<GemmK1>{});
+
+    const auto out_gemmk0_gemmm_gemmk1_grid_desc = descs[I0];
+    const auto in_gemmk0_gemmn_gemmk1_grid_desc  = descs[I1];
+    const auto wei_gemmm_gemmn_grid_desc         = descs[I2];
+
+    // HACK: hacks that control index calculation when iterating over A, B, C matrix
+    constexpr auto out_gemmk0_gemmm_gemmk1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 1, 0, 0>{},   // 0+: GemmK0
+                              Sequence<0, 0, 0, 0, 0>{},   // 1+: GemmM
+                              Sequence<0, 0, 1, 0, 0>{}),  // 2+: GemmK1
+                   make_tuple(Sequence<0, 0, 2, 0, 0>{},   // 0-: GemmK0
+                              Sequence<0, 0, 0, 0, 0>{},   // 1-: GemmM
+                              Sequence<0, 0, 2, 0, 0>{})); // 2-: GemmK1
+
+    constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},   // 0+: GemmK0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},   // 1+: GemmN
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{}),  // 2+: GemmK1
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},   // 0-: GemmK0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},   // 1-: GemmN
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{})); // 2-: GemmK1
+
+    constexpr auto wei_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0+: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2+: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3+: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4+: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5+: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6+: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 7+: N2
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
+
+    constexpr auto out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
+        Sequence<0, 0, 1, 0, 0>{};
+
+    constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
+        Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0>{};
+
+    for(index_t i = 0; i < 5; ++i)
+    {
+        float ave_time = driver_gemm_xdlops_v2r3<
+            BlockSize,
+            TInWei,
+            TAcc,
+            TOut,
+            InMemoryDataOperationEnum_t::Set,
+            decltype(out_gemmk0_gemmm_gemmk1_grid_desc),
+            decltype(in_gemmk0_gemmn_gemmk1_grid_desc),
+            decltype(wei_gemmm_gemmn_grid_desc),
+            GemmMPerBlock,
+            GemmNPerBlock,
+            GemmKPerBlock,
+            GemmMPerWave,
+            GemmNPerWave,
+            GemmK1,
+            MRepeat,
+            NRepeat,
+            GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1,
+            GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1,
+            Sequence<1, 0, 2>,
+            Sequence<1, 0, 2>,
+            2,
+            GemmABlockTransferSrcScalarPerVector_GemmK1,
+            GemmABlockTransferDstScalarPerVector_GemmK1,
+            false, // don't move back src coordinate after threadwise copy
+            GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1,
+            GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1,
+            Sequence<1, 0, 2>,
+            Sequence<1, 0, 2>,
+            2,
+            GemmBBlockTransferSrcScalarPerVector_GemmN,
+            GemmBBlockTransferDstScalarPerVector_GemmK1,
+            false, // don't move back src coordinate after threadwise copy
+            Sequence<3, 0, 1, 2, 7, 5, 4, 6>,
+            7,
+            GemmCThreadTransferDstScalarPerVector,
+            decltype(out_gemmk0_gemmm_gemmk1_grid_step_hacks),
+            decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks),
+            decltype(wei_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
+            decltype(out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
+            decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
+            false>(static_cast<TOut*>(out_n_k_ho_wo_device_buf.GetDeviceBuffer()),
+                   static_cast<TInWei*>(in_n_c_hi_wi_device_buf.GetDeviceBuffer()),
+                   static_cast<TInWei*>(wei_k_c_y_x_device_buf.GetDeviceBuffer()),
+                   out_gemmk0_gemmm_gemmk1_grid_desc,
+                   in_gemmk0_gemmn_gemmk1_grid_desc,
+                   wei_gemmm_gemmn_grid_desc,
+                   out_gemmk0_gemmm_gemmk1_grid_step_hacks,
+                   in_gemmk0_gemmn_gemmk1_grid_step_hacks,
+                   wei_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
+                   out_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
+                   in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
+                   nrepeat);
+
+        float perf = static_cast<float>(calculate_convolution_flops(
+                         in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc)) /
+                     (std::size_t(1000) * 1000 * 1000) / ave_time;
+
+        std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
+    }
+
+    // copy result back to host
+    wei_k_c_y_x_device_buf.FromDevice(wei_k_c_y_x.mData.data());
+}

host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp

@@ -1,280 +0,0 @@
-#include <unistd.h>
-#include "device.hpp"
-#include "host_tensor.hpp"
-#include "driver_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp"
-
-template <typename TInWei,
-          typename TAcc,
-          typename TOut,
-          typename InLengths,
-          typename WeiLengths,
-          typename OutLengths,
-          typename ConvStrides,
-          typename ConvDilations,
-          typename InLeftPads,
-          typename InRightPads>
-void device_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw(
-    const InLengths& in_n_c_hi_wi_lengths,
-    const WeiLengths& wei_k_c_y_x_lengths,
-    const OutLengths& out_n_k_ho_wo_lengths,
-    const ConvStrides& conv_strides,
-    const ConvDilations& conv_dilations,
-    const InLeftPads& in_left_pads,
-    const InRightPads& in_right_pads,
-    const Tensor<TInWei>& in_n_c_hi_wi,
-    const Tensor<TInWei>& wei_k_c_y_x,
-    Tensor<TOut>& out_n_k_ho_wo,
-    ck::index_t nrepeat)
-{
-    using namespace ck;
-
-    std::cout << __func__ << std::endl;
-
-    constexpr auto I0 = Number<0>{};
-    constexpr auto I1 = Number<1>{};
-    constexpr auto I2 = Number<2>{};
-    constexpr auto I3 = Number<3>{};
-    constexpr auto I4 = Number<4>{};
-    constexpr auto I5 = Number<5>{};
-    constexpr auto I6 = Number<6>{};
-    constexpr auto I7 = Number<7>{};
-    constexpr auto I8 = Number<8>{};
-
-    DeviceMem in_n_c_hi_wi_device_buf(sizeof(TInWei) * in_n_c_hi_wi.mDesc.GetElementSpace());
-    DeviceMem wei_k_c_y_x_device_buf(sizeof(TInWei) * wei_k_c_y_x.mDesc.GetElementSpace());
-    DeviceMem out_n_k_ho_wo_device_buf(sizeof(TOut) * out_n_k_ho_wo.mDesc.GetElementSpace());
-
-    in_n_c_hi_wi_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
-    wei_k_c_y_x_device_buf.ToDevice(wei_k_c_y_x.mData.data());
-    out_n_k_ho_wo_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
-
-    const auto in_n_c_hi_wi_desc  = make_naive_tensor_descriptor_packed(in_n_c_hi_wi_lengths);
-    const auto wei_k_c_y_x_desc   = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
-    const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
-
-#if 0
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 128;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 64;
-    constexpr index_t GemmNPerWave = 64;
-    constexpr index_t GemmKPack    = 8;
-
-    constexpr index_t MRepeat = 1;
-    constexpr index_t NRepeat = 1;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 2, 8>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK = 8;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_KPack = 8;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 4, 4>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 32, 2>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 4;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_KPack = 4;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector_GemmN1 = 1;
-#elif 0
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 256;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 64;
-    constexpr index_t GemmNPerWave = 64;
-    constexpr index_t GemmKPack    = 8;
-
-    constexpr index_t MRepeat = 2;
-    constexpr index_t NRepeat = 1;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 4, 8>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK = 8;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_KPack = 8;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 4, 4>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 32, 2>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 4;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_KPack = 4;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector_GemmN1 = 1;
-#elif 0
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 256;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 64;
-    constexpr index_t GemmNPerWave = 64;
-    constexpr index_t GemmKPack    = 8;
-
-    constexpr index_t MRepeat = 2;
-    constexpr index_t NRepeat = 1;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 4, 8>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK = 8;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_KPack = 8;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 4, 4>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 32, 2>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_KPack = 4;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector_GemmN1 = 1;
-#elif 1
-    // [M, N, K0, K1] = [256, 128, 4, 4]
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 256;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 64;
-    constexpr index_t GemmNPerWave = 64;
-    constexpr index_t GemmKPack    = 4;
-
-    constexpr index_t MRepeat = 2;
-    constexpr index_t NRepeat = 1;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 4, 4>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK = 4;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_KPack = 4;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 4>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_KPack = 4;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector_GemmN1 = 1;
-#elif 1
-    // [M, N, K0, K1] = [128, 128, 4, 4]
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 128;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 64;
-    constexpr index_t GemmNPerWave = 64;
-    constexpr index_t GemmKPack    = 4;
-
-    constexpr index_t MRepeat = 1;
-    constexpr index_t NRepeat = 1;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 2, 4>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK = 4;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_KPack = 4;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 4>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN = 1;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_KPack = 4;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector_GemmN1 = 1;
-#endif
-
-    const auto descs =
-#if 1
-        transform_forward_convolution_into_gemm_v4r4_xdlops_nchw_kcyx_nkhw_pad
-#else
-        transform_forward_convolution_into_gemm_v4r4_xdlops_nchw_kcyx_nkhw_1x1
-#endif
-        <TInWei, GemmMPerBlock, GemmNPerBlock, GemmMPerWave, GemmNPerWave, GemmKPack>(
-            wei_k_c_y_x_desc,
-            in_n_c_hi_wi_desc,
-            out_n_k_ho_wo_desc,
-            conv_strides,
-            conv_dilations,
-            in_left_pads,
-            in_right_pads);
-
-    for(index_t i = 0; i < 5; ++i)
-    {
-#if 0
-        float ave_time = launch_kernel_gemm_xdlops_v1
-#else
-        float ave_time = launch_kernel_gemm_xdlops_v2
-#endif
-        <BlockSize,
-         TInWei,
-         TAcc,
-         TOut,
-         InMemoryDataOperationEnum_t::Set,
-         decltype(descs[I0]),
-         decltype(descs[I1]),
-         decltype(descs[I2]),
-         decltype(descs[I3]),
-         GemmMPerBlock,
-         GemmNPerBlock,
-         GemmKPerBlock,
-         GemmMPerWave,
-         GemmNPerWave,
-         GemmKPack,
-         MRepeat,
-         NRepeat,
-         GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1,
-         GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1,
-         Sequence<1, 0, 2>,
-         Sequence<1, 0, 2>,
-         2,
-         GemmABlockTransferSrcScalarPerVector_GemmK,
-         GemmABlockTransferDstScalarPerVector_KPack,
-         false, // don't move back src coordinate after threadwise copy
-         GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1,
-         GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1,
-         Sequence<0, 2, 1>,
-         Sequence<1, 0, 2>,
-         1,
-         GemmBBlockTransferSrcScalarPerVector_GemmN,
-         GemmBBlockTransferDstScalarPerVector_KPack,
-         false, // don't move back src coordinate after threadwise copy, which will be fused
-                // with MoveSrcSliceWindow() to save addr computation
-         Sequence<2, 3, 0, 1>,
-         3,
-         GemmCThreadTransferDstScalarPerVector_GemmN1,
-         decltype(descs[I4]),
-         decltype(descs[I5]),
-         decltype(descs[I6]),
-         decltype(descs[I7]),
-         decltype(descs[I8])>(static_cast<TInWei*>(wei_k_c_y_x_device_buf.GetDeviceBuffer()),
-                              static_cast<TInWei*>(in_n_c_hi_wi_device_buf.GetDeviceBuffer()),
-                              static_cast<TOut*>(out_n_k_ho_wo_device_buf.GetDeviceBuffer()),
-                              descs[I0],
-                              descs[I1],
-                              descs[I2],
-                              descs[I3],
-                              descs[I4],
-                              descs[I5],
-                              descs[I6],
-                              descs[I7],
-                              descs[I8],
-                              nrepeat);
-
-        float perf = (float)calculate_convolution_flops(
-                         in_n_c_hi_wi_desc, wei_k_c_y_x_desc, out_n_k_ho_wo_desc) /
-                     (std::size_t(1000) * 1000 * 1000) / ave_time;
-
-        std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
-    }
-
-    // copy result back to host
-    out_n_k_ho_wo_device_buf.FromDevice(out_n_k_ho_wo.mData.data());
-}

host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp

@@ -47,7 +47,35 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
     const auto wei_k_c_y_x_desc   = make_naive_tensor_descriptor_packed(wei_k_c_y_x_lengths);
     const auto out_n_k_ho_wo_desc = make_naive_tensor_descriptor_packed(out_n_k_ho_wo_lengths);
 
-#if 1
+#if 0
+    // [M, N, K0, K1] = [128, 128, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t GemmMPerBlock = 128;
+    constexpr index_t GemmNPerBlock = 128;
+    constexpr index_t GemmKPerBlock = 4;
+
+    constexpr index_t GemmMPerWave = 32;
+    constexpr index_t GemmNPerWave = 32;
+    constexpr index_t GemmK1       = 8;
+
+    constexpr index_t MRepeat = 2;
+    constexpr index_t NRepeat = 2;
+
+    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 2, 8>;
+    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
+
+    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
+    constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
+
+    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 8>;
+    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
+
+    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmN  = 1;
+    constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
+
+    constexpr index_t GemmCThreadTransferDstScalarPerVector = 1;
+#elif 1
     // [M, N, K0, K1] = [256, 128, 4, 8] for fp16
     constexpr index_t BlockSize = 256;
 
@@ -92,36 +120,39 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
     const auto out_gemmm_gemmn_grid_desc         = descs[I2];
 
     // HACK: hacks that control index calculation when iterating over A, B, C matrix
-    constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple(
-        make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
-        make_tuple(
-            Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}));
+    constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},   // 0+: GemmK0
+                              Sequence<0, 0, 0, 0, 0>{},   // 1+: GemmM
+                              Sequence<0, 0, 0, 0, 0>{}),  // 2+: GemmK1
+                   make_tuple(Sequence<0, 0, 0, 0, 0>{},   // 0-: GemmK0
+                              Sequence<0, 0, 0, 0, 0>{},   // 1-: GemmM
+                              Sequence<0, 0, 0, 0, 0>{})); // 2-: GemmK1
 
     constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
-        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
-                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}));
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},   // 0+: GemmK0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},   // 1+: GemmN
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),  // 2+: GemmK1
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},   // 0-: GemmK0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},   // 1-: GemmN
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{})); // 2-: GemmK1
 
-    constexpr auto out_m0_m1_m2_n_grid_step_hacks =
-        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 1, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 1, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 1, 0, 0>{}),
-                   make_tuple(Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 2, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 2, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 2, 0, 0>{}));
+    constexpr auto out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0+: M0
+                              Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2+: M1
+                              Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3+: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4+: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5+: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6+: M4
+                              Sequence<0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 7+: N2
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: M0
+                              Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: M1
+                              Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M4
+                              Sequence<0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
 
     constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
         Sequence<0, 0, 0, 0, 0>{};
@@ -169,7 +200,7 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
             GemmCThreadTransferDstScalarPerVector,
             decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks),
             decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks),
-            decltype(out_m0_m1_m2_n_grid_step_hacks),
+            decltype(out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
             decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
             decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
             false>(static_cast<TInWei*>(wei_k_c_y_x_device_buf.GetDeviceBuffer()),
@@ -180,7 +211,7 @@ void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw(
                    out_gemmm_gemmn_grid_desc,
                    wei_gemmk0_gemmm_gemmk1_grid_step_hacks,
                    in_gemmk0_gemmn_gemmk1_grid_step_hacks,
-                   out_m0_m1_m2_n_grid_step_hacks,
+                   out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
                    wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
                    in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
                    nrepeat);

host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nhwk.hpp

@@ -1,229 +0,0 @@
-#include <unistd.h>
-#include "device.hpp"
-#include "host_tensor.hpp"
-#include "transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk.hpp"
-#include "driver_gemm_xdlops_v2r2.hpp"
-
-template <typename TInWei,
-          typename TAcc,
-          typename TOut,
-          typename InLengths,
-          typename WeiLengths,
-          typename OutLengths,
-          typename ConvStrides,
-          typename ConvDilations,
-          typename InLeftPads,
-          typename InRightPads>
-void device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nhwc_kyxc_nhwk(
-    const InLengths& in_n_hi_wi_c_lengths,
-    const WeiLengths& wei_k_y_x_c_lengths,
-    const OutLengths& out_n_ho_wo_k_lengths,
-    const ConvStrides& conv_strides,
-    const ConvDilations& conv_dilations,
-    const InLeftPads& in_left_pads,
-    const InRightPads& in_right_pads,
-    const Tensor<TInWei>& in_n_hi_wi_c,
-    const Tensor<TInWei>& wei_k_y_x_c,
-    Tensor<TOut>& out_n_ho_wo_k,
-    ck::index_t nrepeat)
-{
-    using namespace ck;
-
-    std::cout << __func__ << std::endl;
-
-    constexpr auto I0 = Number<0>{};
-    constexpr auto I1 = Number<1>{};
-    constexpr auto I2 = Number<2>{};
-    constexpr auto I3 = Number<3>{};
-
-    DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
-    DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
-    DeviceMem out_n_ho_wo_k_device_buf(sizeof(TOut) * out_n_ho_wo_k.mDesc.GetElementSpace());
-
-    in_n_hi_wi_c_device_buf.ToDevice(in_n_hi_wi_c.mData.data());
-    wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
-    out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data());
-
-    const auto in_n_hi_wi_c_desc  = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths);
-    const auto wei_k_y_x_c_desc   = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths);
-    const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths);
-
-#if 1
-    // [M, N, K0, K1] = [256, 128, 4, 4] for fp32
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 256;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 64;
-    constexpr index_t GemmNPerWave = 64;
-    constexpr index_t GemmK1       = 4;
-
-    constexpr index_t MRepeat = 2;
-    constexpr index_t NRepeat = 1;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 4, 4>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 4>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 4;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 4;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector = 4;
-#elif 1
-    // [M, N, K0, K1] = [256, 128, 4, 8] for fp16
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 256;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 64;
-    constexpr index_t GemmNPerWave = 64;
-    constexpr index_t GemmK1       = 8;
-
-    constexpr index_t MRepeat = 2;
-    constexpr index_t NRepeat = 1;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 4, 8>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 8>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector = 4;
-#endif
-
-    const auto descs =
-        transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk_pad(wei_k_y_x_c_desc,
-                                                                          in_n_hi_wi_c_desc,
-                                                                          out_n_ho_wo_k_desc,
-                                                                          conv_strides,
-                                                                          conv_dilations,
-                                                                          in_left_pads,
-                                                                          in_right_pads,
-                                                                          Number<GemmK1>{});
-
-    const auto wei_gemmk0_gemmm_gemmk1_grid_desc = descs[I0];
-    const auto in_gemmk0_gemmn_gemmk1_grid_desc  = descs[I1];
-    const auto out_gemmm_gemmn_grid_desc         = descs[I2];
-
-    // HACK: hacks that control index calculation when iterating over A, B, C matrix
-    constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple(
-        make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
-        make_tuple(
-            Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}));
-
-    constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
-        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
-                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}));
-
-    constexpr auto out_m0_m1_m2_n_grid_step_hacks =
-        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 1, 0, 0>{}),
-                   make_tuple(Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 2, 0, 0>{}));
-
-    constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
-        Sequence<0, 0, 0, 0, 0>{};
-
-    constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
-        Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
-
-    for(index_t i = 0; i < 5; ++i)
-    {
-        float ave_time = driver_gemm_xdlops_v2r2<
-            BlockSize,
-            TInWei,
-            TAcc,
-            TOut,
-            InMemoryDataOperationEnum_t::Set,
-            decltype(wei_gemmk0_gemmm_gemmk1_grid_desc),
-            decltype(in_gemmk0_gemmn_gemmk1_grid_desc),
-            decltype(out_gemmm_gemmn_grid_desc),
-            GemmMPerBlock,
-            GemmNPerBlock,
-            GemmKPerBlock,
-            GemmMPerWave,
-            GemmNPerWave,
-            MRepeat,
-            NRepeat,
-            GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1,
-            GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1,
-            Sequence<1, 0, 2>,
-            Sequence<1, 0, 2>,
-            2,
-            GemmABlockTransferSrcScalarPerVector_GemmK1,
-            GemmABlockTransferDstScalarPerVector_GemmK1,
-            false, // don't move back src coordinate after threadwise copy
-            GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1,
-            GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1,
-            Sequence<1, 0, 2>,
-            Sequence<1, 0, 2>,
-            2,
-            GemmBBlockTransferSrcScalarPerVector_GemmK1,
-            GemmBBlockTransferDstScalarPerVector_GemmK1,
-            false, // don't move back src coordinate after threadwise copy
-            Sequence<2, 3, 0, 1>,
-            2,
-            GemmCThreadTransferDstScalarPerVector,
-            decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks),
-            decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks),
-            decltype(out_m0_m1_m2_n_grid_step_hacks),
-            decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
-            decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks)>(
-            static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
-            static_cast<TInWei*>(in_n_hi_wi_c_device_buf.GetDeviceBuffer()),
-            static_cast<TOut*>(out_n_ho_wo_k_device_buf.GetDeviceBuffer()),
-            wei_gemmk0_gemmm_gemmk1_grid_desc,
-            in_gemmk0_gemmn_gemmk1_grid_desc,
-            out_gemmm_gemmn_grid_desc,
-            wei_gemmk0_gemmm_gemmk1_grid_step_hacks,
-            in_gemmk0_gemmn_gemmk1_grid_step_hacks,
-            out_m0_m1_m2_n_grid_step_hacks,
-            wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
-            in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
-            nrepeat);
-
-        {
-            const auto N = out_n_ho_wo_k_lengths[I0];
-            const auto K = out_n_ho_wo_k_lengths[I3];
-            const auto C = wei_k_y_x_c_lengths[I3];
-
-            const auto Ho = out_n_ho_wo_k_lengths[I1];
-            const auto Wo = out_n_ho_wo_k_lengths[I2];
-
-            const auto Y = wei_k_y_x_c_lengths[I1];
-            const auto X = wei_k_y_x_c_lengths[I2];
-
-            float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
-                         (std::size_t(1000) * 1000 * 1000) / ave_time;
-
-            std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
-                      << std::endl;
-        }
-    }
-
-    // copy result back to host
-    out_n_ho_wo_k_device_buf.FromDevice(out_n_ho_wo_k.mData.data());
-}

host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk.hpp

@@ -1,302 +0,0 @@
-#include <unistd.h>
-#include "device.hpp"
-#include "host_tensor.hpp"
-#include "transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk.hpp"
-#include "driver_gemm_xdlops_v2r3.hpp"
-
-template <typename TInWei,
-          typename TAcc,
-          typename TOut,
-          typename InLengths,
-          typename WeiLengths,
-          typename OutLengths,
-          typename ConvStrides,
-          typename ConvDilations,
-          typename InLeftPads,
-          typename InRightPads>
-void device_convolution_forward_implicit_gemm_v4r4r3_xdlops_nhwc_kyxc_nhwk(
-    const InLengths& in_n_hi_wi_c_lengths,
-    const WeiLengths& wei_k_y_x_c_lengths,
-    const OutLengths& out_n_ho_wo_k_lengths,
-    const ConvStrides& conv_strides,
-    const ConvDilations& conv_dilations,
-    const InLeftPads& in_left_pads,
-    const InRightPads& in_right_pads,
-    const Tensor<TInWei>& in_n_hi_wi_c,
-    const Tensor<TInWei>& wei_k_y_x_c,
-    Tensor<TOut>& out_n_ho_wo_k,
-    ck::index_t nrepeat)
-{
-    using namespace ck;
-
-    std::cout << __func__ << std::endl;
-
-    constexpr auto I0 = Number<0>{};
-    constexpr auto I1 = Number<1>{};
-    constexpr auto I2 = Number<2>{};
-    constexpr auto I3 = Number<3>{};
-    constexpr auto I4 = Number<4>{};
-    constexpr auto I5 = Number<5>{};
-    constexpr auto I6 = Number<6>{};
-    constexpr auto I7 = Number<7>{};
-    constexpr auto I8 = Number<8>{};
-
-    DeviceMem in_n_hi_wi_c_device_buf(sizeof(TInWei) * in_n_hi_wi_c.mDesc.GetElementSpace());
-    DeviceMem wei_k_y_x_c_device_buf(sizeof(TInWei) * wei_k_y_x_c.mDesc.GetElementSpace());
-    DeviceMem out_n_ho_wo_k_device_buf(sizeof(TOut) * out_n_ho_wo_k.mDesc.GetElementSpace());
-
-    in_n_hi_wi_c_device_buf.ToDevice(in_n_hi_wi_c.mData.data());
-    wei_k_y_x_c_device_buf.ToDevice(wei_k_y_x_c.mData.data());
-    out_n_ho_wo_k_device_buf.ToDevice(out_n_ho_wo_k.mData.data());
-
-    const auto in_n_hi_wi_c_desc  = make_naive_tensor_descriptor_packed(in_n_hi_wi_c_lengths);
-    const auto wei_k_y_x_c_desc   = make_naive_tensor_descriptor_packed(wei_k_y_x_c_lengths);
-    const auto out_n_ho_wo_k_desc = make_naive_tensor_descriptor_packed(out_n_ho_wo_k_lengths);
-
-#if 1
-    // [M, N, K0, K1] = [256, 128, 4, 4] for fp32
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 256;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 4;
-
-    constexpr index_t MRepeat = 4;
-    constexpr index_t NRepeat = 2;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 4, 4>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 4>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 4;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 4;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector = 4;
-#elif 1
-    // [M, N, K0, K1] = [128, 128, 4, 4] for fp32
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 128;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 4;
-
-    constexpr index_t MRepeat = 2;
-    constexpr index_t NRepeat = 2;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 2, 4>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 4;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 4;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 4>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 4;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 4;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector = 4;
-#elif 0
-    // [M, N, K0, K1] = [256, 256, 4, 8] for fp16
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 256;
-    constexpr index_t GemmNPerBlock = 256;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 8;
-
-    constexpr index_t MRepeat = 4;
-    constexpr index_t NRepeat = 4;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 4, 8>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 4, 8>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector = 4;
-#elif 1
-    // [M, N, K0, K1] = [256, 128, 4, 8] for fp16
-    constexpr index_t BlockSize = 256;
-
-    constexpr index_t GemmMPerBlock = 256;
-    constexpr index_t GemmNPerBlock = 128;
-    constexpr index_t GemmKPerBlock = 4;
-
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 8;
-
-    constexpr index_t MRepeat = 4;
-    constexpr index_t NRepeat = 2;
-
-    using GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1   = Sequence<1, 4, 8>;
-    using GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmABlockTransferSrcScalarPerVector_GemmK1 = 8;
-    constexpr index_t GemmABlockTransferDstScalarPerVector_GemmK1 = 8;
-
-    using GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1   = Sequence<1, 2, 8>;
-    using GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1 = Sequence<4, 64, 1>;
-
-    constexpr index_t GemmBBlockTransferSrcScalarPerVector_GemmK1 = 8;
-    constexpr index_t GemmBBlockTransferDstScalarPerVector_GemmK1 = 8;
-
-    constexpr index_t GemmCThreadTransferDstScalarPerVector = 4;
-#endif
-
-    const auto descs =
-        transform_forward_convolution_into_gemm_v4r4r2_nhwc_kyxc_nhwk_pad(wei_k_y_x_c_desc,
-                                                                          in_n_hi_wi_c_desc,
-                                                                          out_n_ho_wo_k_desc,
-                                                                          conv_strides,
-                                                                          conv_dilations,
-                                                                          in_left_pads,
-                                                                          in_right_pads,
-                                                                          Number<GemmK1>{});
-
-    const auto wei_gemmk0_gemmm_gemmk1_grid_desc = descs[I0];
-    const auto in_gemmk0_gemmn_gemmk1_grid_desc  = descs[I1];
-    const auto out_gemmm_gemmn_grid_desc         = descs[I2];
-
-    // HACK: hacks that control index calculation when iterating over A, B, C matrix
-    constexpr auto wei_gemmk0_gemmm_gemmk1_grid_step_hacks = make_tuple(
-        make_tuple(Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}),
-        make_tuple(
-            Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}, Sequence<0, 0, 0, 0, 0>{}));
-
-    constexpr auto in_gemmk0_gemmn_gemmk1_grid_step_hacks =
-        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0>{}),
-                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0>{}));
-
-    constexpr auto out_m0_m1_m2_n_grid_step_hacks =
-        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 1, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 1, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 1, 0, 0>{}),
-                   make_tuple(Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 2, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 2, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 0, 0, 0>{},
-                              Sequence<0, 0, 2, 0, 0>{}));
-
-    constexpr auto wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
-        Sequence<0, 0, 0, 0, 0>{};
-
-    constexpr auto in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks =
-        Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
-
-    for(index_t i = 0; i < 5; ++i)
-    {
-        float ave_time = driver_gemm_xdlops_v2r3<
-            BlockSize,
-            TInWei,
-            TAcc,
-            TOut,
-            InMemoryDataOperationEnum_t::Set,
-            decltype(wei_gemmk0_gemmm_gemmk1_grid_desc),
-            decltype(in_gemmk0_gemmn_gemmk1_grid_desc),
-            decltype(out_gemmm_gemmn_grid_desc),
-            GemmMPerBlock,
-            GemmNPerBlock,
-            GemmKPerBlock,
-            GemmMPerWave,
-            GemmNPerWave,
-            MRepeat,
-            NRepeat,
-            GemmABlockTransferThreadSliceLengths_GemmK0_GemmM_GemmK1,
-            GemmABlockTransferThreadClusterLengths_GemmK0_GemmM_GemmK1,
-            Sequence<1, 0, 2>,
-            Sequence<1, 0, 2>,
-            2,
-            GemmABlockTransferSrcScalarPerVector_GemmK1,
-            GemmABlockTransferDstScalarPerVector_GemmK1,
-            false, // don't move back src coordinate after threadwise copy
-            GemmBBlockTransferThreadSliceLengths_GemmK0_GemmN_GemmK1,
-            GemmBBlockTransferThreadClusterLengths_GemmK0_GemmN_GemmK1,
-            Sequence<1, 0, 2>,
-            Sequence<1, 0, 2>,
-            2,
-            GemmBBlockTransferSrcScalarPerVector_GemmK1,
-            GemmBBlockTransferDstScalarPerVector_GemmK1,
-            false, // don't move back src coordinate after threadwise copy
-            Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
-            6,
-            GemmCThreadTransferDstScalarPerVector,
-            decltype(wei_gemmk0_gemmm_gemmk1_grid_step_hacks),
-            decltype(in_gemmk0_gemmn_gemmk1_grid_step_hacks),
-            decltype(out_m0_m1_m2_n_grid_step_hacks),
-            decltype(wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
-            decltype(in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
-            false // CAccessOrderMRepeatNRepeat
-            >(static_cast<TInWei*>(wei_k_y_x_c_device_buf.GetDeviceBuffer()),
-              static_cast<TInWei*>(in_n_hi_wi_c_device_buf.GetDeviceBuffer()),
-              static_cast<TOut*>(out_n_ho_wo_k_device_buf.GetDeviceBuffer()),
-              wei_gemmk0_gemmm_gemmk1_grid_desc,
-              in_gemmk0_gemmn_gemmk1_grid_desc,
-              out_gemmm_gemmn_grid_desc,
-              wei_gemmk0_gemmm_gemmk1_grid_step_hacks,
-              in_gemmk0_gemmn_gemmk1_grid_step_hacks,
-              out_m0_m1_m2_n_grid_step_hacks,
-              wei_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
-              in_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
-              nrepeat);
-
-        {
-            const auto N = out_n_ho_wo_k_lengths[I0];
-            const auto K = out_n_ho_wo_k_lengths[I3];
-            const auto C = wei_k_y_x_c_lengths[I3];
-
-            const auto Hi = in_n_hi_wi_c_lengths[I1];
-            const auto Wi = in_n_hi_wi_c_lengths[I2];
-
-            const auto Ho = out_n_ho_wo_k_lengths[I1];
-            const auto Wo = out_n_ho_wo_k_lengths[I2];
-
-            const auto Y = wei_k_y_x_c_lengths[I1];
-            const auto X = wei_k_y_x_c_lengths[I2];
-
-            float perf = (float)(std::size_t(2) * N * K * Ho * Wo * C * Y * X) /
-                         (std::size_t(1000) * 1000 * 1000) / ave_time;
-
-            std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s"
-                      << std::endl;
-        }
-    }
-
-    // copy result back to host
-    out_n_ho_wo_k_device_buf.FromDevice(out_n_ho_wo_k.mData.data());
-}

host/driver_offline/include/device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp

@@ -56,8 +56,8 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 128;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
     constexpr index_t GemmK1       = 4;
 
     constexpr index_t MRepeat = 4;
@@ -84,9 +84,9 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 128;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 4;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 4;
 
     constexpr index_t MRepeat = 2;
     constexpr index_t NRepeat = 2;
@@ -112,9 +112,9 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 256;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 8;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 8;
 
     constexpr index_t MRepeat = 4;
     constexpr index_t NRepeat = 4;
@@ -140,9 +140,9 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 128;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 8;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 8;
 
     constexpr index_t MRepeat = 4;
     constexpr index_t NRepeat = 2;
@@ -168,9 +168,9 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 256;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 8;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 8;
 
     constexpr index_t MRepeat = 2;
     constexpr index_t NRepeat = 4;
@@ -196,9 +196,9 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
     constexpr index_t GemmNPerBlock = 128;
     constexpr index_t GemmKPerBlock = 4;
 
-    constexpr index_t GemmMPerWave = 32;
-    constexpr index_t GemmNPerWave = 32;
-    constexpr index_t GemmK1       = 8;
+    constexpr index_t GemmMPerXDL = 32;
+    constexpr index_t GemmNPerXDL = 32;
+    constexpr index_t GemmK1      = 8;
 
     constexpr index_t MRepeat = 2;
     constexpr index_t NRepeat = 2;
@@ -249,23 +249,23 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
                               Sequence<0, 0, 0, 0, 0>{},   // 1-: GemmN
                               Sequence<0, 0, 0, 0, 0>{})); // 2-: GemmK1
 
-    constexpr auto out_m0_m1_m2_n_grid_step_hacks =
-        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0>{},   // 0+: MRepeat
-                              Sequence<0, 0, 0, 0, 0>{},   // 1+: NRepeat
-                              Sequence<0, 0, 0, 0, 0>{},   // 2+: MWaves
-                              Sequence<0, 0, 0, 0, 0>{},   // 3+: NWaves
-                              Sequence<0, 0, 0, 0, 0>{},   // 4+: M0
-                              Sequence<0, 0, 0, 0, 0>{},   // 5+: M1
-                              Sequence<0, 0, 0, 0, 0>{},   // 6+: M2
-                              Sequence<0, 0, 0, 0, 0>{}),  // 7+: N1
-                   make_tuple(Sequence<0, 0, 0, 0, 0>{},   // 0-: MRepeat
-                              Sequence<0, 0, 0, 0, 0>{},   // 1-: NRepeat
-                              Sequence<0, 0, 0, 0, 0>{},   // 2-: MWaves
-                              Sequence<0, 0, 0, 0, 0>{},   // 3-: NWaves
-                              Sequence<0, 0, 0, 0, 0>{},   // 4-: M0
-                              Sequence<0, 0, 0, 0, 0>{},   // 5-: M1
-                              Sequence<0, 0, 0, 0, 0>{},   // 6-: M2
-                              Sequence<0, 0, 0, 0, 0>{})); // 7-: N1
+    constexpr auto out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0+: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2+: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3+: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4+: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5+: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6+: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 7+: N2
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
 
     constexpr auto in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks =
         Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0>{};
@@ -287,8 +287,8 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
             GemmMPerBlock,
             GemmNPerBlock,
             GemmKPerBlock,
-            GemmMPerWave,
-            GemmNPerWave,
+            GemmMPerXDL,
+            GemmNPerXDL,
             GemmK1,
             MRepeat,
             NRepeat,
@@ -313,7 +313,7 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
             GemmCThreadTransferDstScalarPerVector,
             decltype(in_gemmk0_gemmm_gemmk1_grid_step_hacks),
             decltype(wei_gemmk0_gemmn_gemmk1_grid_step_hacks),
-            decltype(out_m0_m1_m2_n_grid_step_hacks),
+            decltype(out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
             decltype(in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks),
             decltype(wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks),
             false // CAccessOrderMRepeatNRepeat
@@ -325,7 +325,7 @@ void device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk(
               out_gemmm_gemmn_grid_desc,
               in_gemmk0_gemmm_gemmk1_grid_step_hacks,
               wei_gemmk0_gemmn_gemmk1_grid_step_hacks,
-              out_m0_m1_m2_n_grid_step_hacks,
+              out_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
               in_gemmk0_gemmm_gemmk1_grid_move_slice_window_step_hacks,
               wei_gemmk0_gemmn_gemmk1_grid_move_slice_window_step_hacks,
               nrepeat);

host/driver_offline/include/device_gemm_xdlops_km_kn_mn.hpp

@@ -0,0 +1,219 @@
+#pragma once
+#include <unistd.h>
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "driver_gemm_xdlops_v2r3.hpp"
+
+template <typename ABType,
+          typename AccType,
+          typename CType,
+          typename ADesc,
+          typename BDesc,
+          typename CDesc>
+void device_gemm_xdlops_km_kn_mn(const ADesc& a_k_m_grid_desc,
+                                 const BDesc& b_k_n_grid_desc,
+                                 const CDesc& c_m_n_grid_desc,
+                                 const Tensor<ABType>& a_k_m,
+                                 const Tensor<ABType>& b_k_n,
+                                 Tensor<CType>& c_m_n,
+                                 ck::index_t nrepeat)
+{
+    using namespace ck;
+
+    std::cout << __func__ << std::endl;
+
+    constexpr auto I0 = Number<0>{};
+    constexpr auto I1 = Number<1>{};
+
+    DeviceMem a_k_m_device_buf(sizeof(ABType) * a_k_m.mDesc.GetElementSpace());
+    DeviceMem b_k_n_device_buf(sizeof(ABType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CType) * c_m_n.mDesc.GetElementSpace());
+
+    a_k_m_device_buf.ToDevice(a_k_m.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+    c_m_n_device_buf.ToDevice(c_m_n.mData.data());
+
+#if 0
+    // [M, N, K0, K1] = [256, 128, 4, 4] for fp32
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 256;
+    constexpr index_t NPerBlock = 128;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 4;
+
+    constexpr index_t MRepeat = 4;
+    constexpr index_t NRepeat = 2;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 4, 4>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_M  = 4;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 2, 4>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_N  = 2;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#elif 1
+    // [M, N, K0, K1] = [256, 128, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 256;
+    constexpr index_t NPerBlock = 128;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 8;
+
+    constexpr index_t MRepeat = 4;
+    constexpr index_t NRepeat = 2;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 4, 8>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_M  = 4;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 2, 8>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_N  = 2;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#endif
+
+    const auto K = a_k_m_grid_desc.GetLength(I0);
+    const auto M = a_k_m_grid_desc.GetLength(I1);
+    const auto N = b_k_n_grid_desc.GetLength(I1);
+
+    constexpr auto K1Number = Number<K1>{};
+    const auto K0           = K / K1Number;
+
+    const auto a_k0_m_k1_grid_desc =
+        transform_tensor_descriptor(a_k_m_grid_desc,
+                                    make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                                               make_pass_through_transform(M)),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+    const auto b_k0_n_k1_grid_desc =
+        transform_tensor_descriptor(b_k_n_grid_desc,
+                                    make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                                               make_pass_through_transform(N)),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+    // HACK: hacks that control index calculation when iterating over A, B, C matrix
+    constexpr auto a_k0_m_k1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0>{},   // 0+: K0
+                              Sequence<0, 0, 0>{},   // 1+: M
+                              Sequence<0, 0, 0>{}),  // 2+: K1
+                   make_tuple(Sequence<0, 0, 0>{},   // 0-: K0
+                              Sequence<0, 0, 0>{},   // 1-: M
+                              Sequence<0, 0, 0>{})); // 2-: K1
+
+    constexpr auto b_k0_n_k1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0>{},   // 0+: K0
+                              Sequence<0, 0, 0>{},   // 1+: N
+                              Sequence<0, 0, 0>{}),  // 2+: K1
+                   make_tuple(Sequence<0, 0, 0>{},   // 0-: K0
+                              Sequence<0, 0, 0>{},   // 1-: N
+                              Sequence<0, 0, 0>{})); // 2-: K1
+
+    constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0+: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2+: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3+: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4+: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5+: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6+: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 7+: N2
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
+
+    constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
+
+    constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
+
+    for(index_t i = 0; i < 5; ++i)
+    {
+        float ave_time =
+            driver_gemm_xdlops_v2r3<BlockSize,
+                                    ABType,
+                                    AccType,
+                                    CType,
+                                    InMemoryDataOperationEnum_t::Set,
+                                    decltype(a_k0_m_k1_grid_desc),
+                                    decltype(b_k0_n_k1_grid_desc),
+                                    decltype(c_m_n_grid_desc),
+                                    MPerBlock,
+                                    NPerBlock,
+                                    KPerBlock,
+                                    MPerXDL,
+                                    NPerXDL,
+                                    K1,
+                                    MRepeat,
+                                    NRepeat,
+                                    ABlockTransferThreadSliceLengths_K0_M_K1,
+                                    ABlockTransferThreadClusterLengths_K0_M_K1,
+                                    Sequence<0, 2, 1>,
+                                    Sequence<0, 2, 1>,
+                                    1,
+                                    ABlockTransferSrcScalarPerVector_M,
+                                    ABlockTransferDstScalarPerVector_K1,
+                                    false, // don't move back src coordinate after threadwise copy
+                                    BBlockTransferThreadSliceLengths_K0_N_K1,
+                                    BBlockTransferThreadClusterLengths_K0_N_K1,
+                                    Sequence<0, 2, 1>,
+                                    Sequence<0, 2, 1>,
+                                    1,
+                                    BBlockTransferSrcScalarPerVector_N,
+                                    BBlockTransferDstScalarPerVector_K1,
+                                    false, // don't move back src coordinate after threadwise copy
+                                    Sequence<0, 2, 4, 5, 6, 1, 3, 7>,
+                                    7,
+                                    CThreadTransferDstScalarPerVector,
+                                    decltype(a_k0_m_k1_grid_step_hacks),
+                                    decltype(b_k0_n_k1_grid_step_hacks),
+                                    decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
+                                    decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
+                                    decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
+                                    false // CAccessOrderMRepeatNRepeat
+                                    >(static_cast<ABType*>(a_k_m_device_buf.GetDeviceBuffer()),
+                                      static_cast<ABType*>(b_k_n_device_buf.GetDeviceBuffer()),
+                                      static_cast<CType*>(c_m_n_device_buf.GetDeviceBuffer()),
+                                      a_k0_m_k1_grid_desc,
+                                      b_k0_n_k1_grid_desc,
+                                      c_m_n_grid_desc,
+                                      a_k0_m_k1_grid_step_hacks,
+                                      b_k0_n_k1_grid_step_hacks,
+                                      c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
+                                      a_k0_m_k1_grid_move_slice_window_step_hacks,
+                                      b_k0_n_k1_grid_move_slice_window_step_hacks,
+                                      nrepeat);
+
+        float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
+                     (std::size_t(1000) * 1000 * 1000) / ave_time;
+
+        std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
+    }
+
+    // copy result back to host
+    c_m_n_device_buf.FromDevice(c_m_n.mData.data());
+}

host/driver_offline/include/device_gemm_xdlops_km_nk_mn.hpp

@@ -0,0 +1,219 @@
+#pragma once
+#include <unistd.h>
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "driver_gemm_xdlops_v2r3.hpp"
+
+template <typename ABType,
+          typename AccType,
+          typename CType,
+          typename ADesc,
+          typename BDesc,
+          typename CDesc>
+void device_gemm_xdlops_km_nk_mn(const ADesc& a_k_m_grid_desc,
+                                 const BDesc& b_n_k_grid_desc,
+                                 const CDesc& c_m_n_grid_desc,
+                                 const Tensor<ABType>& a_k_m,
+                                 const Tensor<ABType>& b_n_k,
+                                 Tensor<CType>& c_m_n,
+                                 ck::index_t nrepeat)
+{
+    using namespace ck;
+
+    std::cout << __func__ << std::endl;
+
+    constexpr auto I0 = Number<0>{};
+    constexpr auto I1 = Number<1>{};
+
+    DeviceMem a_k_m_device_buf(sizeof(ABType) * a_k_m.mDesc.GetElementSpace());
+    DeviceMem b_n_k_device_buf(sizeof(ABType) * b_n_k.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CType) * c_m_n.mDesc.GetElementSpace());
+
+    a_k_m_device_buf.ToDevice(a_k_m.mData.data());
+    b_n_k_device_buf.ToDevice(b_n_k.mData.data());
+    c_m_n_device_buf.ToDevice(c_m_n.mData.data());
+
+#if 0
+    // [M, N, K0, K1] = [256, 128, 4, 4] for fp32
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 256;
+    constexpr index_t NPerBlock = 128;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 4;
+
+    constexpr index_t MRepeat = 4;
+    constexpr index_t NRepeat = 2;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 4, 4>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_M  = 4;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 2, 4>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#elif 1
+    // [M, N, K0, K1] = [256, 128, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 256;
+    constexpr index_t NPerBlock = 128;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 8;
+
+    constexpr index_t MRepeat = 4;
+    constexpr index_t NRepeat = 2;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 4, 8>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_M  = 4;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 2, 8>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#endif
+
+    const auto K = a_k_m_grid_desc.GetLength(I0);
+    const auto M = a_k_m_grid_desc.GetLength(I1);
+    const auto N = b_n_k_grid_desc.GetLength(I0);
+
+    constexpr auto K1Number = Number<K1>{};
+    const auto K0           = K / K1Number;
+
+    const auto a_k0_m_k1_grid_desc =
+        transform_tensor_descriptor(a_k_m_grid_desc,
+                                    make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                                               make_pass_through_transform(M)),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+    const auto b_k0_n_k1_grid_desc =
+        transform_tensor_descriptor(b_n_k_grid_desc,
+                                    make_tuple(make_pass_through_transform(N),
+                                               make_unmerge_transform(make_tuple(K0, K1Number))),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
+
+    // HACK: hacks that control index calculation when iterating over A, B, C matrix
+    constexpr auto a_k0_m_k1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0>{},   // 0+: K0
+                              Sequence<0, 0, 0>{},   // 1+: M
+                              Sequence<0, 0, 0>{}),  // 2+: K1
+                   make_tuple(Sequence<0, 0, 0>{},   // 0-: K0
+                              Sequence<0, 0, 0>{},   // 1-: M
+                              Sequence<0, 0, 0>{})); // 2-: K1
+
+    constexpr auto b_k0_n_k1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0>{},   // 0+: K0
+                              Sequence<0, 0, 0>{},   // 1+: N
+                              Sequence<0, 0, 0>{}),  // 2+: K1
+                   make_tuple(Sequence<0, 0, 0>{},   // 0-: K0
+                              Sequence<0, 0, 0>{},   // 1-: N
+                              Sequence<0, 0, 0>{})); // 2-: K1
+
+    constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0+: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2+: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3+: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4+: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5+: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6+: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 7+: N2
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
+
+    constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
+
+    constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
+
+    for(index_t i = 0; i < 5; ++i)
+    {
+        float ave_time =
+            driver_gemm_xdlops_v2r3<BlockSize,
+                                    ABType,
+                                    AccType,
+                                    CType,
+                                    InMemoryDataOperationEnum_t::Set,
+                                    decltype(a_k0_m_k1_grid_desc),
+                                    decltype(b_k0_n_k1_grid_desc),
+                                    decltype(c_m_n_grid_desc),
+                                    MPerBlock,
+                                    NPerBlock,
+                                    KPerBlock,
+                                    MPerXDL,
+                                    NPerXDL,
+                                    K1,
+                                    MRepeat,
+                                    NRepeat,
+                                    ABlockTransferThreadSliceLengths_K0_M_K1,
+                                    ABlockTransferThreadClusterLengths_K0_M_K1,
+                                    Sequence<0, 2, 1>,
+                                    Sequence<0, 2, 1>,
+                                    1,
+                                    ABlockTransferSrcScalarPerVector_M,
+                                    ABlockTransferDstScalarPerVector_K1,
+                                    false, // don't move back src coordinate after threadwise copy
+                                    BBlockTransferThreadSliceLengths_K0_N_K1,
+                                    BBlockTransferThreadClusterLengths_K0_N_K1,
+                                    Sequence<1, 0, 2>,
+                                    Sequence<1, 0, 2>,
+                                    2,
+                                    BBlockTransferSrcScalarPerVector_K1,
+                                    BBlockTransferDstScalarPerVector_K1,
+                                    false, // don't move back src coordinate after threadwise copy
+                                    Sequence<0, 2, 4, 5, 6, 1, 3, 7>,
+                                    7,
+                                    CThreadTransferDstScalarPerVector,
+                                    decltype(a_k0_m_k1_grid_step_hacks),
+                                    decltype(b_k0_n_k1_grid_step_hacks),
+                                    decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
+                                    decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
+                                    decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
+                                    false // CAccessOrderMRepeatNRepeat
+                                    >(static_cast<ABType*>(a_k_m_device_buf.GetDeviceBuffer()),
+                                      static_cast<ABType*>(b_n_k_device_buf.GetDeviceBuffer()),
+                                      static_cast<CType*>(c_m_n_device_buf.GetDeviceBuffer()),
+                                      a_k0_m_k1_grid_desc,
+                                      b_k0_n_k1_grid_desc,
+                                      c_m_n_grid_desc,
+                                      a_k0_m_k1_grid_step_hacks,
+                                      b_k0_n_k1_grid_step_hacks,
+                                      c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
+                                      a_k0_m_k1_grid_move_slice_window_step_hacks,
+                                      b_k0_n_k1_grid_move_slice_window_step_hacks,
+                                      nrepeat);
+
+        float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
+                     (std::size_t(1000) * 1000 * 1000) / ave_time;
+
+        std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
+    }
+
+    // copy result back to host
+    c_m_n_device_buf.FromDevice(c_m_n.mData.data());
+}

host/driver_offline/include/device_gemm_xdlops_mk_kn_mn.hpp

@@ -0,0 +1,219 @@
+#pragma once
+#include <unistd.h>
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "driver_gemm_xdlops_v2r3.hpp"
+
+template <typename ABType,
+          typename AccType,
+          typename CType,
+          typename ADesc,
+          typename BDesc,
+          typename CDesc>
+void device_gemm_xdlops_mk_kn_mn(const ADesc& a_m_k_grid_desc,
+                                 const BDesc& b_k_n_grid_desc,
+                                 const CDesc& c_m_n_grid_desc,
+                                 const Tensor<ABType>& a_m_k,
+                                 const Tensor<ABType>& b_k_n,
+                                 Tensor<CType>& c_m_n,
+                                 ck::index_t nrepeat)
+{
+    using namespace ck;
+
+    std::cout << __func__ << std::endl;
+
+    constexpr auto I0 = Number<0>{};
+    constexpr auto I1 = Number<1>{};
+
+    DeviceMem a_m_k_device_buf(sizeof(ABType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_k_n_device_buf(sizeof(ABType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CType) * c_m_n.mDesc.GetElementSpace());
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+    c_m_n_device_buf.ToDevice(c_m_n.mData.data());
+
+#if 1
+    // [M, N, K0, K1] = [256, 128, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 256;
+    constexpr index_t NPerBlock = 128;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 8;
+
+    constexpr index_t MRepeat = 4;
+    constexpr index_t NRepeat = 2;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 4, 8>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 2, 8>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_N  = 2;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#elif 0
+    // [M, N, K0, K1] = [128, 256, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 128;
+    constexpr index_t NPerBlock = 256;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 8;
+
+    constexpr index_t MRepeat = 2;
+    constexpr index_t NRepeat = 4;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 2, 8>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 4, 8>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_N  = 4;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#endif
+
+    const auto K = a_m_k_grid_desc.GetLength(I1);
+    const auto M = a_m_k_grid_desc.GetLength(I0);
+    const auto N = b_k_n_grid_desc.GetLength(I1);
+
+    constexpr auto K1Number = Number<K1>{};
+    const auto K0           = K / K1Number;
+
+    const auto a_k0_m_k1_grid_desc =
+        transform_tensor_descriptor(a_m_k_grid_desc,
+                                    make_tuple(make_pass_through_transform(M),
+                                               make_unmerge_transform(make_tuple(K0, K1Number))),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
+
+    const auto b_k0_n_k1_grid_desc =
+        transform_tensor_descriptor(b_k_n_grid_desc,
+                                    make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                                               make_pass_through_transform(N)),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+    // HACK: hacks that control index calculation when iterating over A, B, C matrix
+    constexpr auto a_k0_m_k1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0>{},   // 0+: K0
+                              Sequence<0, 0, 0>{},   // 1+: M
+                              Sequence<0, 0, 0>{}),  // 2+: K1
+                   make_tuple(Sequence<0, 0, 0>{},   // 0-: K0
+                              Sequence<0, 0, 0>{},   // 1-: M
+                              Sequence<0, 0, 0>{})); // 2-: K1
+
+    constexpr auto b_k0_n_k1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0>{},   // 0+: K0
+                              Sequence<0, 0, 0>{},   // 1+: N
+                              Sequence<0, 0, 0>{}),  // 2+: K1
+                   make_tuple(Sequence<0, 0, 0>{},   // 0-: K0
+                              Sequence<0, 0, 0>{},   // 1-: N
+                              Sequence<0, 0, 0>{})); // 2-: K1
+
+    constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0+: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2+: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3+: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4+: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5+: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6+: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 7+: N2
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
+
+    constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
+
+    constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
+
+    for(index_t i = 0; i < 5; ++i)
+    {
+        float ave_time =
+            driver_gemm_xdlops_v2r3<BlockSize,
+                                    ABType,
+                                    AccType,
+                                    CType,
+                                    InMemoryDataOperationEnum_t::Set,
+                                    decltype(a_k0_m_k1_grid_desc),
+                                    decltype(b_k0_n_k1_grid_desc),
+                                    decltype(c_m_n_grid_desc),
+                                    MPerBlock,
+                                    NPerBlock,
+                                    KPerBlock,
+                                    MPerXDL,
+                                    NPerXDL,
+                                    K1,
+                                    MRepeat,
+                                    NRepeat,
+                                    ABlockTransferThreadSliceLengths_K0_M_K1,
+                                    ABlockTransferThreadClusterLengths_K0_M_K1,
+                                    Sequence<1, 0, 2>,
+                                    Sequence<1, 0, 2>,
+                                    2,
+                                    ABlockTransferSrcScalarPerVector_K1,
+                                    ABlockTransferDstScalarPerVector_K1,
+                                    false, // don't move back src coordinate after threadwise copy
+                                    BBlockTransferThreadSliceLengths_K0_N_K1,
+                                    BBlockTransferThreadClusterLengths_K0_N_K1,
+                                    Sequence<0, 2, 1>,
+                                    Sequence<0, 2, 1>,
+                                    1,
+                                    BBlockTransferSrcScalarPerVector_N,
+                                    BBlockTransferDstScalarPerVector_K1,
+                                    false, // don't move back src coordinate after threadwise copy
+                                    Sequence<0, 2, 4, 5, 6, 1, 3, 7>,
+                                    7,
+                                    CThreadTransferDstScalarPerVector,
+                                    decltype(a_k0_m_k1_grid_step_hacks),
+                                    decltype(b_k0_n_k1_grid_step_hacks),
+                                    decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
+                                    decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
+                                    decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
+                                    false // CAccessOrderMRepeatNRepeat
+                                    >(static_cast<ABType*>(a_m_k_device_buf.GetDeviceBuffer()),
+                                      static_cast<ABType*>(b_k_n_device_buf.GetDeviceBuffer()),
+                                      static_cast<CType*>(c_m_n_device_buf.GetDeviceBuffer()),
+                                      a_k0_m_k1_grid_desc,
+                                      b_k0_n_k1_grid_desc,
+                                      c_m_n_grid_desc,
+                                      a_k0_m_k1_grid_step_hacks,
+                                      b_k0_n_k1_grid_step_hacks,
+                                      c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
+                                      a_k0_m_k1_grid_move_slice_window_step_hacks,
+                                      b_k0_n_k1_grid_move_slice_window_step_hacks,
+                                      nrepeat);
+
+        float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
+                     (std::size_t(1000) * 1000 * 1000) / ave_time;
+
+        std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
+    }
+
+    // copy result back to host
+    c_m_n_device_buf.FromDevice(c_m_n.mData.data());
+}

host/driver_offline/include/device_gemm_xdlops_mk_nk_mn.hpp

@@ -0,0 +1,275 @@
+#pragma once
+#include <unistd.h>
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "driver_gemm_xdlops_v2r3.hpp"
+
+template <typename ABType,
+          typename AccType,
+          typename CType,
+          typename ADesc,
+          typename BDesc,
+          typename CDesc>
+void device_gemm_xdlops_mk_nk_mn(const ADesc& a_m_k_grid_desc,
+                                 const BDesc& b_n_k_grid_desc,
+                                 const CDesc& c_m_n_grid_desc,
+                                 const Tensor<ABType>& a_m_k,
+                                 const Tensor<ABType>& b_n_k,
+                                 Tensor<CType>& c_m_n,
+                                 ck::index_t nrepeat)
+{
+    using namespace ck;
+
+    std::cout << __func__ << std::endl;
+
+    constexpr auto I0 = Number<0>{};
+    constexpr auto I1 = Number<1>{};
+
+    DeviceMem a_m_k_device_buf(sizeof(ABType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_n_k_device_buf(sizeof(ABType) * b_n_k.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CType) * c_m_n.mDesc.GetElementSpace());
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_n_k_device_buf.ToDevice(b_n_k.mData.data());
+    c_m_n_device_buf.ToDevice(c_m_n.mData.data());
+
+#if 0
+    // [M, N, K0, K1] = [128, 256, 4, 4] for fp32
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 128;
+    constexpr index_t NPerBlock = 256;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 4;
+
+    constexpr index_t MRepeat = 2;
+    constexpr index_t NRepeat = 4;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 2, 4>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 4;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 4;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 4, 4>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 4;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 4;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#elif 1
+    // [M, N, K0, K1] = [256, 128, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 256;
+    constexpr index_t NPerBlock = 128;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 8;
+
+    constexpr index_t MRepeat = 4;
+    constexpr index_t NRepeat = 2;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 4, 8>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 2, 8>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#elif 0
+    // [M, N, K0, K1] = [128, 256, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 128;
+    constexpr index_t NPerBlock = 256;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 8;
+
+    constexpr index_t MRepeat = 2;
+    constexpr index_t NRepeat = 4;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 2, 8>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 4, 8>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#elif 1
+    // [M, N, K0, K1] = [128, 128, 4, 8] for fp16
+    constexpr index_t BlockSize = 256;
+
+    constexpr index_t MPerBlock = 128;
+    constexpr index_t NPerBlock = 128;
+    constexpr index_t KPerBlock = 4;
+
+    constexpr index_t MPerXDL = 32;
+    constexpr index_t NPerXDL = 32;
+    constexpr index_t K1      = 8;
+
+    constexpr index_t MRepeat = 2;
+    constexpr index_t NRepeat = 2;
+
+    using ABlockTransferThreadSliceLengths_K0_M_K1   = Sequence<1, 2, 8>;
+    using ABlockTransferThreadClusterLengths_K0_M_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t ABlockTransferSrcScalarPerVector_K1 = 8;
+    constexpr index_t ABlockTransferDstScalarPerVector_K1 = 8;
+
+    using BBlockTransferThreadSliceLengths_K0_N_K1   = Sequence<1, 2, 8>;
+    using BBlockTransferThreadClusterLengths_K0_N_K1 = Sequence<4, 64, 1>;
+
+    constexpr index_t BBlockTransferSrcScalarPerVector_K1 = 8;
+    constexpr index_t BBlockTransferDstScalarPerVector_K1 = 8;
+
+    constexpr index_t CThreadTransferDstScalarPerVector = 1;
+#endif
+
+    const auto K = a_m_k_grid_desc.GetLength(I1);
+    const auto M = a_m_k_grid_desc.GetLength(I0);
+    const auto N = b_n_k_grid_desc.GetLength(I0);
+
+    constexpr auto K1Number = Number<K1>{};
+    const auto K0           = K / K1Number;
+
+    const auto a_k0_m_k1_grid_desc =
+        transform_tensor_descriptor(a_m_k_grid_desc,
+                                    make_tuple(make_pass_through_transform(M),
+                                               make_unmerge_transform(make_tuple(K0, K1Number))),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
+
+    const auto b_k0_n_k1_grid_desc =
+        transform_tensor_descriptor(b_n_k_grid_desc,
+                                    make_tuple(make_pass_through_transform(N),
+                                               make_unmerge_transform(make_tuple(K0, K1Number))),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
+
+    // HACK: hacks that control index calculation when iterating over A, B, C matrix
+    constexpr auto a_k0_m_k1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0>{},   // 0+: K0
+                              Sequence<0, 0, 0>{},   // 1+: M
+                              Sequence<0, 0, 0>{}),  // 2+: K1
+                   make_tuple(Sequence<0, 0, 0>{},   // 0-: K0
+                              Sequence<0, 0, 0>{},   // 1-: M
+                              Sequence<0, 0, 0>{})); // 2-: K1
+
+    constexpr auto b_k0_n_k1_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0>{},   // 0+: K0
+                              Sequence<0, 0, 0>{},   // 1+: N
+                              Sequence<0, 0, 0>{}),  // 2+: K1
+                   make_tuple(Sequence<0, 0, 0>{},   // 0-: K0
+                              Sequence<0, 0, 0>{},   // 1-: N
+                              Sequence<0, 0, 0>{})); // 2-: K1
+
+    constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks =
+        make_tuple(make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0+: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1+: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2+: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3+: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4+: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5+: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6+: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{}),  // 7+: N2
+                   make_tuple(Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 0-: M0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 1-: N0
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 2-: M1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 3-: N1
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 4-: M2
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 5-: M3
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{},   // 6-: M4
+                              Sequence<0, 0, 0, 0, 0, 0, 0, 0, 0>{})); // 7-: N2
+
+    constexpr auto a_k0_m_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
+
+    constexpr auto b_k0_n_k1_grid_move_slice_window_step_hacks = Sequence<0, 0, 0>{};
+
+    for(index_t i = 0; i < 5; ++i)
+    {
+        float ave_time =
+            driver_gemm_xdlops_v2r3<BlockSize,
+                                    ABType,
+                                    AccType,
+                                    CType,
+                                    InMemoryDataOperationEnum_t::Set,
+                                    decltype(a_k0_m_k1_grid_desc),
+                                    decltype(b_k0_n_k1_grid_desc),
+                                    decltype(c_m_n_grid_desc),
+                                    MPerBlock,
+                                    NPerBlock,
+                                    KPerBlock,
+                                    MPerXDL,
+                                    NPerXDL,
+                                    K1,
+                                    MRepeat,
+                                    NRepeat,
+                                    ABlockTransferThreadSliceLengths_K0_M_K1,
+                                    ABlockTransferThreadClusterLengths_K0_M_K1,
+                                    Sequence<1, 0, 2>,
+                                    Sequence<1, 0, 2>,
+                                    2,
+                                    ABlockTransferSrcScalarPerVector_K1,
+                                    ABlockTransferDstScalarPerVector_K1,
+                                    false, // don't move back src coordinate after threadwise copy
+                                    BBlockTransferThreadSliceLengths_K0_N_K1,
+                                    BBlockTransferThreadClusterLengths_K0_N_K1,
+                                    Sequence<1, 0, 2>,
+                                    Sequence<1, 0, 2>,
+                                    2,
+                                    BBlockTransferSrcScalarPerVector_K1,
+                                    BBlockTransferDstScalarPerVector_K1,
+                                    false, // don't move back src coordinate after threadwise copy
+                                    Sequence<0, 2, 4, 5, 6, 1, 3, 7>,
+                                    7,
+                                    CThreadTransferDstScalarPerVector,
+                                    decltype(a_k0_m_k1_grid_step_hacks),
+                                    decltype(b_k0_n_k1_grid_step_hacks),
+                                    decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks),
+                                    decltype(a_k0_m_k1_grid_move_slice_window_step_hacks),
+                                    decltype(b_k0_n_k1_grid_move_slice_window_step_hacks),
+                                    false // CAccessOrderMRepeatNRepeat
+                                    >(static_cast<ABType*>(a_m_k_device_buf.GetDeviceBuffer()),
+                                      static_cast<ABType*>(b_n_k_device_buf.GetDeviceBuffer()),
+                                      static_cast<CType*>(c_m_n_device_buf.GetDeviceBuffer()),
+                                      a_k0_m_k1_grid_desc,
+                                      b_k0_n_k1_grid_desc,
+                                      c_m_n_grid_desc,
+                                      a_k0_m_k1_grid_step_hacks,
+                                      b_k0_n_k1_grid_step_hacks,
+                                      c_m0_n0_m1_n1_m2_m3_m4_n2_grid_step_hacks,
+                                      a_k0_m_k1_grid_move_slice_window_step_hacks,
+                                      b_k0_n_k1_grid_move_slice_window_step_hacks,
+                                      nrepeat);
+
+        float perf = static_cast<float>((std::size_t(2) * M * N * K)) /
+                     (std::size_t(1000) * 1000 * 1000) / ave_time;
+
+        std::cout << "Average time : " << ave_time << " ms, " << perf << " TFlop/s" << std::endl;
+    }
+
+    // copy result back to host
+    c_m_n_device_buf.FromDevice(c_m_n.mData.data());
+}

host/driver_offline/include/driver_gemm_xdlops_v2r3.hpp

@@ -17,8 +17,8 @@ template <ck::index_t BlockSize,
           ck::index_t MPerBlock,
           ck::index_t NPerBlock,
           ck::index_t KPerBlock,
-          ck::index_t MPerWave,
-          ck::index_t NPerWave,
+          ck::index_t MPerXDL,
+          ck::index_t NPerXDL,
           ck::index_t K1,
           ck::index_t MRepeat,
           ck::index_t NRepeat,
@@ -79,8 +79,8 @@ __host__ float driver_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
                                                 MPerBlock,
                                                 NPerBlock,
                                                 KPerBlock,
-                                                MPerWave,
-                                                NPerWave,
+                                                MPerXDL,
+                                                NPerXDL,
                                                 K1,
                                                 MRepeat,
                                                 NRepeat,
@@ -129,9 +129,10 @@ __host__ float driver_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
             "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v2r3 has invalid setting");
     }
 
-    const auto c_m0_m1_m2_n_grid_desc = GridwiseGemm::MakeCM0M1M2NGridDescriptor(c_m_n_grid_desc);
+    const auto c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc =
+        GridwiseGemm::MakeCM0N0M1N1M2M3M4N2GridDescriptor(c_m_n_grid_desc);
 
-    using CM0M1M2NGridDesc = decltype(c_m0_m1_m2_n_grid_desc);
+    using CM0N0M1N1M2M3M4N2GridDesc = decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc);
 
     const auto c_block_cluster_adaptor = GridwiseGemm::MakeCBlockClusterAdaptor(c_m_n_grid_desc);
 
@@ -144,7 +145,7 @@ __host__ float driver_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
                                                 FloatC,
                                                 remove_reference_t<AK0MK1GridDesc>,
                                                 remove_reference_t<BK0NK1GridDesc>,
-                                                remove_reference_t<CM0M1M2NGridDesc>,
+                                                remove_reference_t<CM0N0M1N1M2M3M4N2GridDesc>,
                                                 remove_reference_t<CBlockClusterAdaptor>>;
 
 #if CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VALUE
@@ -158,18 +159,18 @@ __host__ float driver_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
                                             p_c_grid,
                                             a_k0_m_k1_grid_desc,
                                             b_k0_n_k1_grid_desc,
-                                            c_m0_m1_m2_n_grid_desc,
+                                            c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc,
                                             c_block_cluster_adaptor);
 
 #elif CK_EXPERIMENTAL_PASS_TENSOR_DESCRIPTOR_BY_VOID_POINTER
     DeviceMem a_k0_m_k1_grid_desc_dev_buf(sizeof(AK0MK1GridDesc));
     DeviceMem b_k0_n_k1_grid_desc_dev_buf(sizeof(BK0NK1GridDesc));
-    DeviceMem c_m0_m1_m2_n_grid_desc_dev_buf(sizeof(CM0M1M2NGridDesc));
+    DeviceMem c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc_dev_buf(sizeof(CM0N0M1N1M2M3M4N2GridDesc));
     DeviceMem c_block_cluster_adaptor_dev_buf(sizeof(CBlockClusterAdaptor));
 
     a_k0_m_k1_grid_desc_dev_buf.ToDevice(&a_k0_m_k1_grid_desc);
     b_k0_n_k1_grid_desc_dev_buf.ToDevice(&b_k0_n_k1_grid_desc);
-    c_m0_m1_m2_n_grid_desc_dev_buf.ToDevice(&c_m0_m1_m2_n_grid_desc);
+    c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc_dev_buf.ToDevice(&c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc);
     c_block_cluster_adaptor_dev_buf.ToDevice(&c_block_cluster_adaptor);
 
     float ave_time = launch_and_time_kernel(
@@ -183,7 +184,8 @@ __host__ float driver_gemm_xdlops_v2r3(const FloatAB* p_a_grid,
         p_c_grid,
         cast_pointer_to_constant_address_space(a_k0_m_k1_grid_desc_dev_buf.GetDeviceBuffer()),
         cast_pointer_to_constant_address_space(b_k0_n_k1_grid_desc_dev_buf.GetDeviceBuffer()),
-        cast_pointer_to_constant_address_space(c_m0_m1_m2_n_grid_desc_dev_buf.GetDeviceBuffer()),
+        cast_pointer_to_constant_address_space(
+            c_m0_n0_m1_n1_m2_m3_m4_n2_grid_desc_dev_buf.GetDeviceBuffer()),
         cast_pointer_to_constant_address_space(c_block_cluster_adaptor_dev_buf.GetDeviceBuffer()));
 #endif
     return ave_time;

host/driver_offline/src/conv_bwd_driver_offline.cpp

@@ -41,7 +41,7 @@ int main(int argc, char* argv[])
     // dynamic mode
     if(argc != 22)
     {
-        printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n");
+        printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
         printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
         exit(1);
     }
@@ -79,7 +79,7 @@ int main(int argc, char* argv[])
     // static mode
     if(argc < 7)
     {
-        printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n");
+        printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
         exit(1);
     }
 
@@ -90,28 +90,28 @@ int main(int argc, char* argv[])
     const bool do_log               = std::stoi(argv[5]);
     const int nrepeat               = std::stoi(argv[6]);
 
-    constexpr index_t N  = 128;
-    constexpr index_t C  = 192;
-    constexpr index_t Hi = 71;
-    constexpr index_t Wi = 71;
-    constexpr index_t K  = 256;
-    constexpr index_t Y  = 3;
-    constexpr index_t X  = 3;
+    constexpr auto N  = Number<128>{};
+    constexpr auto C  = Number<192>{};
+    constexpr auto Hi = Number<71>{};
+    constexpr auto Wi = Number<71>{};
+    constexpr auto K  = Number<256>{};
+    constexpr auto Y  = Number<3>{};
+    constexpr auto X  = Number<3>{};
 
-    const index_t conv_stride_h   = 2;
-    const index_t conv_stride_w   = 2;
-    const index_t conv_dilation_h = 1;
-    const index_t conv_dilation_w = 1;
-    const index_t in_left_pad_h   = 1;
-    const index_t in_left_pad_w   = 1;
-    const index_t in_right_pad_h  = 1;
-    const index_t in_right_pad_w  = 1;
+    constexpr auto conv_stride_h   = I2;
+    constexpr auto conv_stride_w   = I2;
+    constexpr auto conv_dilation_h = I1;
+    constexpr auto conv_dilation_w = I1;
+    constexpr auto in_left_pad_h   = I1;
+    constexpr auto in_left_pad_w   = I1;
+    constexpr auto in_right_pad_h  = I1;
+    constexpr auto in_right_pad_w  = I1;
 
-    const index_t YEff = (Y - 1) * conv_dilation_h + 1;
-    const index_t XEff = (X - 1) * conv_dilation_w + 1;
+    constexpr auto YEff = (Y - I1) * conv_dilation_h + I1;
+    constexpr auto XEff = (X - I1) * conv_dilation_w + I1;
 
-    const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
-    const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
+    constexpr auto Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + I1;
+    constexpr auto Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + I1;
 #endif
 
 #if 0
@@ -119,9 +119,9 @@ int main(int argc, char* argv[])
     using acc_data_t                 = float;
     using out_data_t                 = float;
 #elif 1
-    using in_data_t  = half_t;
-    using acc_data_t = float;
-    using out_data_t = half_t;
+    using in_data_t   = half_t;
+    using acc_data_t  = float;
+    using out_data_t  = half_t;
 #endif
 
     std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);

host/driver_offline/src/conv_fwd_driver_offline.cpp

@@ -19,13 +19,13 @@
 #include "device_convolution_forward_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
 #include "device_convolution_forward_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
 
-#define USE_MODE 1
-#define USE_CONV_FWD_V4R4_NCHW 1
-#define USE_CONV_FWD_V4R4R2_NHWC 1
+#define USE_DYNAMIC_MODE 1
+#define USE_CONV_FWD_V4R4_NCHW 0
+#define USE_CONV_FWD_V4R4R2_NHWC 0
 #define USE_CONV_FWD_V6R1_NCHW 0
 #define USE_CONV_FWD_V5R1_NCHW 0
-#define USE_CONV_FWD_V4R4R2_XDL_NCHW 0
-#define USE_CONV_FWD_V4R4R4_XDL_NHWC 0
+#define USE_CONV_FWD_V4R4R2_XDL_NCHW 1
+#define USE_CONV_FWD_V4R4R4_XDL_NHWC 1
 
 enum ConvForwardAlgo
 {
@@ -49,11 +49,11 @@ int main(int argc, char* argv[])
     constexpr auto I5 = Number<5>{};
     constexpr auto I6 = Number<6>{};
 
-#if USE_MODE
+#if USE_DYNAMIC_MODE
     // dynamic mode
     if(argc != 22)
     {
-        printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n");
+        printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
         printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
         exit(1);
     }
@@ -91,7 +91,7 @@ int main(int argc, char* argv[])
     // static mode
     if(argc < 7)
     {
-        printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n");
+        printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
         exit(1);
     }
 
@@ -102,38 +102,38 @@ int main(int argc, char* argv[])
     const bool do_log             = std::stoi(argv[5]);
     const int nrepeat             = std::stoi(argv[6]);
 
-    constexpr index_t N  = 128;
-    constexpr index_t C  = 192;
-    constexpr index_t Hi = 71;
-    constexpr index_t Wi = 71;
-    constexpr index_t K  = 256;
-    constexpr index_t Y  = 3;
-    constexpr index_t X  = 3;
+    constexpr auto N  = Number<128>{};
+    constexpr auto C  = Number<192>{};
+    constexpr auto Hi = Number<71>{};
+    constexpr auto Wi = Number<71>{};
+    constexpr auto K  = Number<256>{};
+    constexpr auto Y  = Number<3>{};
+    constexpr auto X  = Number<3>{};
 
-    const index_t conv_stride_h   = 2;
-    const index_t conv_stride_w   = 2;
-    const index_t conv_dilation_h = 1;
-    const index_t conv_dilation_w = 1;
-    const index_t in_left_pad_h   = 1;
-    const index_t in_left_pad_w   = 1;
-    const index_t in_right_pad_h  = 1;
-    const index_t in_right_pad_w  = 1;
+    constexpr auto conv_stride_h   = I2;
+    constexpr auto conv_stride_w   = I2;
+    constexpr auto conv_dilation_h = I1;
+    constexpr auto conv_dilation_w = I1;
+    constexpr auto in_left_pad_h   = I1;
+    constexpr auto in_left_pad_w   = I1;
+    constexpr auto in_right_pad_h  = I1;
+    constexpr auto in_right_pad_w  = I1;
 
-    const index_t YEff = (Y - 1) * conv_dilation_h + 1;
-    const index_t XEff = (X - 1) * conv_dilation_w + 1;
+    constexpr auto YEff = (Y - I1) * conv_dilation_h + I1;
+    constexpr auto XEff = (X - I1) * conv_dilation_w + I1;
 
-    const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
-    const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
+    constexpr auto Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + I1;
+    constexpr auto Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + I1;
 #endif
 
-#if 1
+#if 0
     using in_data_t  = float;
     using acc_data_t = float;
     using out_data_t = float;
 #elif 1
-    using in_data_t  = half_t;
-    using acc_data_t = float;
-    using out_data_t = half_t;
+    using in_data_t   = half_t;
+    using acc_data_t  = float;
+    using out_data_t  = half_t;
 #elif 1
     using in_data_t  = int8_t;
     using acc_data_t = int32_t;
@@ -228,7 +228,6 @@ int main(int argc, char* argv[])
     }
 
     auto f_make_for_device_nchw = [&]() {
-#if USE_MODE
         const auto in_lengths_dev     = make_tuple(N, C, Hi, Wi);
         const auto wei_lengths_dev    = make_tuple(K, C, Y, X);
         const auto out_lengths_dev    = make_tuple(N, K, Ho, Wo);
@@ -236,19 +235,6 @@ int main(int argc, char* argv[])
         const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
         const auto in_left_pads_dev   = make_tuple(in_left_pad_h, in_left_pad_w);
         const auto in_right_pads_dev  = make_tuple(in_right_pad_h, in_right_pad_w);
-#else
-        const auto in_lengths_dev =
-            make_tuple(Number<N>{}, Number<C>{}, Number<Hi>{}, Number<Wi>{});
-        const auto wei_lengths_dev = make_tuple(Number<K>{}, Number<C>{}, Number<Y>{}, Number<X>{});
-        const auto out_lengths_dev =
-            make_tuple(Number<N>{}, Number<K>{}, Number<Ho>{}, Number<Wo>{});
-        const auto conv_strides_dev = make_tuple(Number<conv_stride_h>{}, Number<conv_stride_w>{});
-        const auto conv_dilations_dev =
-            make_tuple(Number<conv_dilation_h>{}, Number<conv_dilation_w>{});
-        const auto in_left_pads_dev = make_tuple(Number<in_left_pad_h>{}, Number<in_left_pad_w>{});
-        const auto in_right_pads_dev =
-            make_tuple(Number<in_right_pad_h>{}, Number<in_right_pad_w>{});
-#endif
 
         return make_tuple(in_lengths_dev,
                           wei_lengths_dev,
@@ -260,7 +246,6 @@ int main(int argc, char* argv[])
     };
 
     auto f_make_for_device_nhwc = [&]() {
-#if USE_MODE
         const auto in_lengths_dev     = make_tuple(N, Hi, Wi, C);
         const auto wei_lengths_dev    = make_tuple(K, Y, X, C);
         const auto out_lengths_dev    = make_tuple(N, Ho, Wo, K);
@@ -268,19 +253,6 @@ int main(int argc, char* argv[])
         const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
         const auto in_left_pads_dev   = make_tuple(in_left_pad_h, in_left_pad_w);
         const auto in_right_pads_dev  = make_tuple(in_right_pad_h, in_right_pad_w);
-#else
-        const auto in_lengths_dev =
-            make_tuple(Number<N>{}, Number<Hi>{}, Number<Wi>{}, Number<C>{});
-        const auto wei_lengths_dev = make_tuple(Number<K>{}, Number<Y>{}, Number<X>{}, Number<C>{});
-        const auto out_lengths_dev =
-            make_tuple(Number<N>{}, Number<Ho>{}, Number<Wo>{}, Number<K>{});
-        const auto conv_strides_dev = make_tuple(Number<conv_stride_h>{}, Number<conv_stride_w>{});
-        const auto conv_dilations_dev =
-            make_tuple(Number<conv_dilation_h>{}, Number<conv_dilation_w>{});
-        const auto in_left_pads_dev = make_tuple(Number<in_left_pad_h>{}, Number<in_left_pad_w>{});
-        const auto in_right_pads_dev =
-            make_tuple(Number<in_right_pad_h>{}, Number<in_right_pad_w>{});
-#endif
 
         return make_tuple(in_lengths_dev,
                           wei_lengths_dev,

host/driver_offline/src/conv_wrw_driver_offline.cpp

@@ -0,0 +1,281 @@
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+#include "config.hpp"
+#include "print.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "conv_common.hpp"
+#include "host_conv_bwd_weight.hpp"
+#include "device_tensor.hpp"
+#include "device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
+
+#define USE_DYNAMIC_MODE 1
+#define USE_CONV_WRW_V4R4R2_XDL_NCHW 1
+
+enum ConvBackwardWeightAlgo
+{
+    V4R4R2XDLNCHW,
+};
+
+int main(int argc, char* argv[])
+{
+    using namespace ck;
+
+    constexpr auto I0 = Number<0>{};
+    constexpr auto I1 = Number<1>{};
+    constexpr auto I2 = Number<2>{};
+    constexpr auto I3 = Number<3>{};
+    constexpr auto I4 = Number<4>{};
+    constexpr auto I5 = Number<5>{};
+    constexpr auto I6 = Number<6>{};
+
+#if USE_DYNAMIC_MODE
+    // dynamic mode
+    if(argc != 22)
+    {
+        printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
+        printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
+        exit(1);
+    }
+
+    const ConvTensorLayout layout     = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
+    const ConvBackwardWeightAlgo algo = static_cast<ConvBackwardWeightAlgo>(std::stoi(argv[2]));
+    const bool do_verification        = std::stoi(argv[3]);
+    const int init_method             = std::stoi(argv[4]);
+    const bool do_log                 = std::stoi(argv[5]);
+    const int nrepeat                 = std::stoi(argv[6]);
+
+    const index_t N  = std::stoi(argv[7]);
+    const index_t K  = std::stoi(argv[8]);
+    const index_t C  = std::stoi(argv[9]);
+    const index_t Y  = std::stoi(argv[10]);
+    const index_t X  = std::stoi(argv[11]);
+    const index_t Hi = std::stoi(argv[12]);
+    const index_t Wi = std::stoi(argv[13]);
+
+    const index_t conv_stride_h   = std::stoi(argv[14]);
+    const index_t conv_stride_w   = std::stoi(argv[15]);
+    const index_t conv_dilation_h = std::stoi(argv[16]);
+    const index_t conv_dilation_w = std::stoi(argv[17]);
+    const index_t in_left_pad_h   = std::stoi(argv[18]);
+    const index_t in_left_pad_w   = std::stoi(argv[19]);
+    const index_t in_right_pad_h  = std::stoi(argv[20]);
+    const index_t in_right_pad_w  = std::stoi(argv[21]);
+
+    const index_t YEff = (Y - 1) * conv_dilation_h + 1;
+    const index_t XEff = (X - 1) * conv_dilation_w + 1;
+
+    const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
+    const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
+#else
+    // static mode
+    if(argc < 7)
+    {
+        printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
+        exit(1);
+    }
+
+    const ConvTensorLayout layout     = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
+    const ConvBackwardWeightAlgo algo = static_cast<ConvBackwardWeightAlgo>(std::stoi(argv[2]));
+    const bool do_verification        = std::stoi(argv[3]);
+    const int init_method             = std::stoi(argv[4]);
+    const bool do_log                 = std::stoi(argv[5]);
+    const int nrepeat                 = std::stoi(argv[6]);
+
+    constexpr auto N  = Number<128>{};
+    constexpr auto C  = Number<128>{};
+    constexpr auto Hi = Number<14>{};
+    constexpr auto Wi = Number<14>{};
+    constexpr auto K  = Number<256>{};
+    constexpr auto Y  = Number<3>{};
+    constexpr auto X  = Number<3>{};
+
+    constexpr auto conv_stride_h   = I1;
+    constexpr auto conv_stride_w   = I1;
+    constexpr auto conv_dilation_h = I1;
+    constexpr auto conv_dilation_w = I1;
+    constexpr auto in_left_pad_h   = I1;
+    constexpr auto in_left_pad_w   = I1;
+    constexpr auto in_right_pad_h  = I1;
+    constexpr auto in_right_pad_w  = I1;
+
+    constexpr auto YEff = (Y - I1) * conv_dilation_h + I1;
+    constexpr auto XEff = (X - I1) * conv_dilation_w + I1;
+
+    constexpr auto Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + I1;
+    constexpr auto Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + I1;
+#endif
+
+#if 1
+    using in_data_t  = float;
+    using acc_data_t = float;
+    using out_data_t = float;
+#elif 1
+    using in_data_t   = half_t;
+    using acc_data_t  = float;
+    using out_data_t  = half_t;
+#elif 1
+    using in_data_t  = int8_t;
+    using acc_data_t = int32_t;
+    using out_data_t = int8_t;
+#endif
+
+    std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
+
+    if(layout == ConvTensorLayout::NCHW)
+    {
+        in_lengths_host[0]  = static_cast<std::size_t>(N);
+        in_lengths_host[1]  = static_cast<std::size_t>(C);
+        in_lengths_host[2]  = static_cast<std::size_t>(Hi);
+        in_lengths_host[3]  = static_cast<std::size_t>(Wi);
+        wei_lengths_host[0] = static_cast<std::size_t>(K);
+        wei_lengths_host[1] = static_cast<std::size_t>(C);
+        wei_lengths_host[2] = static_cast<std::size_t>(Y);
+        wei_lengths_host[3] = static_cast<std::size_t>(X);
+        out_lengths_host[0] = static_cast<std::size_t>(N);
+        out_lengths_host[1] = static_cast<std::size_t>(K);
+        out_lengths_host[2] = static_cast<std::size_t>(Ho);
+        out_lengths_host[3] = static_cast<std::size_t>(Wo);
+    }
+    else if(layout == ConvTensorLayout::NHWC)
+    {
+        in_lengths_host[0]  = static_cast<std::size_t>(N);
+        in_lengths_host[1]  = static_cast<std::size_t>(Hi);
+        in_lengths_host[2]  = static_cast<std::size_t>(Wi);
+        in_lengths_host[3]  = static_cast<std::size_t>(C);
+        wei_lengths_host[0] = static_cast<std::size_t>(K);
+        wei_lengths_host[1] = static_cast<std::size_t>(Y);
+        wei_lengths_host[2] = static_cast<std::size_t>(X);
+        wei_lengths_host[3] = static_cast<std::size_t>(C);
+        out_lengths_host[0] = static_cast<std::size_t>(N);
+        out_lengths_host[1] = static_cast<std::size_t>(Ho);
+        out_lengths_host[2] = static_cast<std::size_t>(Wo);
+        out_lengths_host[3] = static_cast<std::size_t>(K);
+    }
+    else
+    {
+        std::runtime_error("wrong! not implemented");
+    }
+
+    Tensor<in_data_t> in(in_lengths_host);
+    Tensor<in_data_t> wei_device(wei_lengths_host);
+    Tensor<out_data_t> wei_host(wei_lengths_host);
+    Tensor<out_data_t> out(out_lengths_host);
+
+    std::cout << "layout: " << layout << std::endl;
+    ostream_HostTensorDescriptor(in.mDesc, std::cout << "in: ");
+    ostream_HostTensorDescriptor(wei_host.mDesc, std::cout << "wei: ");
+    ostream_HostTensorDescriptor(out.mDesc, std::cout << "out: ");
+    print_array("InLeftPads", make_tuple(in_left_pad_h, in_left_pad_w));
+    print_array("InRightPads", make_tuple(in_right_pad_h, in_right_pad_w));
+    print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w));
+    print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w));
+
+    std::size_t num_thread = std::thread::hardware_concurrency();
+
+    switch(init_method)
+    {
+    case 0:
+        // no initialization
+        break;
+    case 1:
+        in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        out.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        break;
+    case 2:
+        in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        out.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+        break;
+    case 3:
+        in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+        out.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        break;
+    case 4:
+        in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+        out.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+        break;
+    case 5:
+        in.GenerateTensorValue(GeneratorTensor_3<float>{-0.1, 0.1}, num_thread);
+        out.GenerateTensorValue(GeneratorTensor_3<float>{-0.1, 0.1}, num_thread);
+        break;
+    default:
+        in.GenerateTensorValue(GeneratorTensor_2{1, 5}, num_thread);
+
+        auto gen_out = [](auto... is) {
+            return GeneratorTensor_2{1, 5}(is...) * GeneratorTensor_Checkboard{}(is...);
+        };
+        out.GenerateTensorValue(gen_out, num_thread);
+    }
+
+    auto f_make_for_device_nchw = [&]() {
+        const auto in_lengths_dev     = make_tuple(N, C, Hi, Wi);
+        const auto wei_lengths_dev    = make_tuple(K, C, Y, X);
+        const auto out_lengths_dev    = make_tuple(N, K, Ho, Wo);
+        const auto conv_strides_dev   = make_tuple(conv_stride_h, conv_stride_w);
+        const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
+        const auto in_left_pads_dev   = make_tuple(in_left_pad_h, in_left_pad_w);
+        const auto in_right_pads_dev  = make_tuple(in_right_pad_h, in_right_pad_w);
+
+        return make_tuple(in_lengths_dev,
+                          wei_lengths_dev,
+                          out_lengths_dev,
+                          conv_strides_dev,
+                          conv_dilations_dev,
+                          in_left_pads_dev,
+                          in_right_pads_dev);
+    };
+
+#if USE_CONV_WRW_V4R4R2_XDL_NCHW
+    if(algo == ConvBackwardWeightAlgo::V4R4R2XDLNCHW)
+    {
+        if(layout != ConvTensorLayout::NCHW)
+        {
+            throw std::runtime_error("wrong! layout");
+        }
+
+        const auto tmp = f_make_for_device_nchw();
+
+        device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw<in_data_t,
+                                                                                      acc_data_t,
+                                                                                      out_data_t>(
+            tmp[I0],
+            tmp[I1],
+            tmp[I2],
+            tmp[I3],
+            tmp[I4],
+            tmp[I5],
+            tmp[I6],
+            in,
+            wei_device,
+            out,
+            nrepeat);
+    }
+#endif
+
+    if(do_verification)
+    {
+        host_direct_convolution_backward_weights(out,
+                                                 in,
+                                                 wei_host,
+                                                 make_tuple(conv_stride_h, conv_stride_w),
+                                                 make_tuple(conv_dilation_h, conv_dilation_w),
+                                                 make_tuple(in_left_pad_h, in_left_pad_w),
+                                                 make_tuple(in_right_pad_h, in_right_pad_w),
+                                                 layout);
+
+        check_error(wei_host, wei_device);
+
+        if(do_log)
+        {
+            LogRangeAsType<float>(std::cout << "out: ", out.mData, ",") << std::endl;
+            LogRangeAsType<float>(std::cout << "in : ", in.mData, ",") << std::endl;
+            LogRangeAsType<float>(std::cout << "wei_device: ", wei_device.mData, ",") << std::endl;
+            LogRangeAsType<float>(std::cout << "wei_host  : ", wei_host.mData, ",") << std::endl;
+        }
+    }
+}

host/driver_offline/src/gemm_driver_offline.cpp

@@ -0,0 +1,294 @@
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+#include "config.hpp"
+#include "print.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "gemm_common.hpp"
+#include "host_gemm.hpp"
+#include "device_tensor.hpp"
+#include "device_gemm_xdlops_mk_kn_mn.hpp"
+#include "device_gemm_xdlops_mk_nk_mn.hpp"
+#include "device_gemm_xdlops_km_kn_mn.hpp"
+#include "device_gemm_xdlops_km_nk_mn.hpp"
+
+#define USE_GEMM_XDL_MK_KN_MN 1
+#define USE_GEMM_XDL_MK_NK_MN 1
+#define USE_GEMM_XDL_KM_KN_MN 1
+#define USE_GEMM_XDL_KM_NK_MN 1
+
+enum GemmAlgo
+{
+    Xdl_MK_KN_MN, // 0
+    Xdl_MK_NK_MN, // 1
+    Xdl_KM_KN_MN, // 2
+    Xdl_KM_NK_MN, // 3
+};
+
+int main(int argc, char* argv[])
+{
+    using namespace ck;
+
+    constexpr auto I0 = Number<0>{};
+    constexpr auto I1 = Number<1>{};
+    constexpr auto I2 = Number<2>{};
+
+    // dynamic mode
+    if(argc != 10)
+    {
+        printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
+        printf("rest: M, N, K\n");
+        exit(1);
+    }
+
+    const auto layout          = static_cast<GemmMatrixLayout>(std::stoi(argv[1]));
+    const auto algo            = static_cast<GemmAlgo>(std::stoi(argv[2]));
+    const bool do_verification = std::stoi(argv[3]);
+    const int init_method      = std::stoi(argv[4]);
+    const bool do_log          = std::stoi(argv[5]);
+    const int nrepeat          = std::stoi(argv[6]);
+
+    const index_t M = std::stoi(argv[7]);
+    const index_t N = std::stoi(argv[8]);
+    const index_t K = std::stoi(argv[9]);
+
+#if 0
+    using ab_data_t  = float;
+    using acc_data_t = float;
+    using c_data_t   = float;
+#elif 1
+    using ab_data_t  = half_t;
+    using acc_data_t = float;
+    using c_data_t   = half_t;
+#elif 1
+    using ab_data_t  = int8_t;
+    using acc_data_t = int32_t;
+    using c_data_t   = int8_t;
+#endif
+
+    std::vector<std::size_t> a_lengths_host(2), b_lengths_host(2), c_lengths_host(2);
+    std::vector<std::size_t> a_strides_host(2), b_strides_host(2), c_strides_host(2);
+
+    if(layout == GemmMatrixLayout::MK_KN_MN)
+    {
+        a_lengths_host[0] = static_cast<std::size_t>(M);
+        a_lengths_host[1] = static_cast<std::size_t>(K);
+        a_strides_host[0] = static_cast<std::size_t>(K);
+        a_strides_host[1] = static_cast<std::size_t>(1);
+
+        b_lengths_host[0] = static_cast<std::size_t>(K);
+        b_lengths_host[1] = static_cast<std::size_t>(N);
+        b_strides_host[0] = static_cast<std::size_t>(N);
+        b_strides_host[1] = static_cast<std::size_t>(1);
+
+        c_lengths_host[0] = static_cast<std::size_t>(M);
+        c_lengths_host[1] = static_cast<std::size_t>(N);
+        c_strides_host[0] = static_cast<std::size_t>(N);
+        c_strides_host[1] = static_cast<std::size_t>(1);
+    }
+    else if(layout == GemmMatrixLayout::MK_NK_MN)
+    {
+        a_lengths_host[0] = static_cast<std::size_t>(M);
+        a_lengths_host[1] = static_cast<std::size_t>(K);
+        a_strides_host[0] = static_cast<std::size_t>(K);
+        a_strides_host[1] = static_cast<std::size_t>(1);
+
+        b_lengths_host[0] = static_cast<std::size_t>(N);
+        b_lengths_host[1] = static_cast<std::size_t>(K);
+        b_strides_host[0] = static_cast<std::size_t>(K);
+        b_strides_host[1] = static_cast<std::size_t>(1);
+
+        c_lengths_host[0] = static_cast<std::size_t>(M);
+        c_lengths_host[1] = static_cast<std::size_t>(N);
+        c_strides_host[0] = static_cast<std::size_t>(N);
+        c_strides_host[1] = static_cast<std::size_t>(1);
+    }
+    else if(layout == GemmMatrixLayout::KM_KN_MN)
+    {
+        a_lengths_host[0] = static_cast<std::size_t>(K);
+        a_lengths_host[1] = static_cast<std::size_t>(M);
+        a_strides_host[0] = static_cast<std::size_t>(M);
+        a_strides_host[1] = static_cast<std::size_t>(1);
+
+        b_lengths_host[0] = static_cast<std::size_t>(K);
+        b_lengths_host[1] = static_cast<std::size_t>(N);
+        b_strides_host[0] = static_cast<std::size_t>(N);
+        b_strides_host[1] = static_cast<std::size_t>(1);
+
+        c_lengths_host[0] = static_cast<std::size_t>(M);
+        c_lengths_host[1] = static_cast<std::size_t>(N);
+        c_strides_host[0] = static_cast<std::size_t>(N);
+        c_strides_host[1] = static_cast<std::size_t>(1);
+    }
+    else if(layout == GemmMatrixLayout::KM_NK_MN)
+    {
+        a_lengths_host[0] = static_cast<std::size_t>(K);
+        a_lengths_host[1] = static_cast<std::size_t>(M);
+        a_strides_host[0] = static_cast<std::size_t>(M);
+        a_strides_host[1] = static_cast<std::size_t>(1);
+
+        b_lengths_host[0] = static_cast<std::size_t>(N);
+        b_lengths_host[1] = static_cast<std::size_t>(K);
+        b_strides_host[0] = static_cast<std::size_t>(K);
+        b_strides_host[1] = static_cast<std::size_t>(1);
+
+        c_lengths_host[0] = static_cast<std::size_t>(M);
+        c_lengths_host[1] = static_cast<std::size_t>(N);
+        c_strides_host[0] = static_cast<std::size_t>(N);
+        c_strides_host[1] = static_cast<std::size_t>(1);
+    }
+    else
+    {
+        std::runtime_error("wrong! not implemented");
+    }
+
+    Tensor<ab_data_t> a(a_lengths_host, a_strides_host);
+    Tensor<ab_data_t> b(b_lengths_host, b_strides_host);
+    Tensor<c_data_t> c_host(c_lengths_host, c_strides_host);
+    Tensor<c_data_t> c_device(c_lengths_host, c_strides_host);
+
+    std::cout << "layout: " << layout << std::endl;
+    ostream_HostTensorDescriptor(a.mDesc, std::cout << "a: ");
+    ostream_HostTensorDescriptor(b.mDesc, std::cout << "b: ");
+    ostream_HostTensorDescriptor(c_host.mDesc, std::cout << "c: ");
+
+    std::size_t num_thread = std::thread::hardware_concurrency();
+
+    switch(init_method)
+    {
+    case 0:
+        // no initialization
+        break;
+    case 1:
+        a.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        b.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        break;
+    case 2:
+        a.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        b.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+        break;
+    case 3:
+        a.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+        b.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        break;
+    case 4:
+        a.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+        b.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+        break;
+    default:
+        a.GenerateTensorValue(GeneratorTensor_3<float>{0.0, 1.0}, num_thread);
+        b.GenerateTensorValue(GeneratorTensor_3<float>{-0.5, 0.5}, num_thread);
+    }
+
+    auto f_make_for_device_mk_kn_mn = [&]() {
+        const auto a_desc = make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(K, I1));
+        const auto b_desc = make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(N, I1));
+        const auto c_desc = make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(N, I1));
+
+        return make_tuple(a_desc, b_desc, c_desc);
+    };
+
+    auto f_make_for_device_mk_nk_mn = [&]() {
+        const auto a_desc = make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(K, I1));
+        const auto b_desc = make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(K, I1));
+        const auto c_desc = make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(N, I1));
+
+        return make_tuple(a_desc, b_desc, c_desc);
+    };
+
+    auto f_make_for_device_km_kn_mn = [&]() {
+        const auto a_desc = make_naive_tensor_descriptor(make_tuple(K, M), make_tuple(M, I1));
+        const auto b_desc = make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(N, I1));
+        const auto c_desc = make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(N, I1));
+
+        return make_tuple(a_desc, b_desc, c_desc);
+    };
+
+    auto f_make_for_device_km_nk_mn = [&]() {
+        const auto a_desc = make_naive_tensor_descriptor(make_tuple(K, M), make_tuple(M, I1));
+        const auto b_desc = make_naive_tensor_descriptor(make_tuple(N, K), make_tuple(K, I1));
+        const auto c_desc = make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(N, I1));
+
+        return make_tuple(a_desc, b_desc, c_desc);
+    };
+
+#if USE_GEMM_XDL_MK_KN_MN
+    if(algo == GemmAlgo::Xdl_MK_KN_MN)
+    {
+        if(layout != GemmMatrixLayout::MK_KN_MN)
+        {
+            throw std::runtime_error("wrong! layout");
+        }
+
+        const auto descs = f_make_for_device_mk_kn_mn();
+
+        device_gemm_xdlops_mk_kn_mn<ab_data_t, acc_data_t, c_data_t>(
+            descs[I0], descs[I1], descs[I2], a, b, c_device, nrepeat);
+    }
+#endif
+
+#if USE_GEMM_XDL_MK_NK_MN
+    if(algo == GemmAlgo::Xdl_MK_NK_MN)
+    {
+        if(layout != GemmMatrixLayout::MK_NK_MN)
+        {
+            throw std::runtime_error("wrong! layout");
+        }
+
+        const auto descs = f_make_for_device_mk_nk_mn();
+
+        device_gemm_xdlops_mk_nk_mn<ab_data_t, acc_data_t, c_data_t>(
+            descs[I0], descs[I1], descs[I2], a, b, c_device, nrepeat);
+    }
+#endif
+
+#if USE_GEMM_XDL_KM_KN_MN
+    if(algo == GemmAlgo::Xdl_KM_KN_MN)
+    {
+        if(layout != GemmMatrixLayout::KM_KN_MN)
+        {
+            throw std::runtime_error("wrong! layout");
+        }
+
+        const auto descs = f_make_for_device_km_kn_mn();
+
+        device_gemm_xdlops_km_kn_mn<ab_data_t, acc_data_t, c_data_t>(
+            descs[I0], descs[I1], descs[I2], a, b, c_device, nrepeat);
+    }
+#endif
+
+#if USE_GEMM_XDL_KM_NK_MN
+    if(algo == GemmAlgo::Xdl_KM_NK_MN)
+    {
+        if(layout != GemmMatrixLayout::KM_NK_MN)
+        {
+            throw std::runtime_error("wrong! layout");
+        }
+
+        const auto descs = f_make_for_device_km_nk_mn();
+
+        device_gemm_xdlops_km_nk_mn<ab_data_t, acc_data_t, c_data_t>(
+            descs[I0], descs[I1], descs[I2], a, b, c_device, nrepeat);
+    }
+#endif
+
+    if(do_verification)
+    {
+        host_gemm(a, b, c_host, layout);
+
+        check_error(c_host, c_device);
+
+        if(do_log)
+        {
+            LogRangeAsType<float>(std::cout << "a : ", a.mData, ",") << std::endl;
+            LogRangeAsType<float>(std::cout << "b: ", b.mData, ",") << std::endl;
+            LogRangeAsType<float>(std::cout << "c_host  : ", c_host.mData, ",") << std::endl;
+            LogRangeAsType<float>(std::cout << "c_device: ", c_device.mData, ",") << std::endl;
+        }
+    }
+}

host/host_tensor/include/gemm_common.hpp

@@ -0,0 +1,12 @@
+#ifndef GEMM_COMMON_HPP
+#define GEMM_COMMON_HPP
+
+enum GemmMatrixLayout
+{
+    MK_KN_MN, // 0
+    MK_NK_MN, // 1
+    KM_KN_MN, // 2
+    KM_NK_MN, // 3
+};
+
+#endif

host/host_tensor/include/host_conv_bwd_weight.hpp

@@ -0,0 +1,89 @@
+#pragma once
+#include "host_tensor.hpp"
+
+template <typename TOut,
+          typename TIn,
+          typename TWei,
+          typename ConvStrides,
+          typename ConvDilations,
+          typename InLeftPads,
+          typename InRightPads>
+void host_direct_convolution_backward_weights(
+    const Tensor<TOut>& out,
+    const Tensor<TIn>& in,
+    Tensor<TWei>& wei,
+    const ConvStrides& conv_strides,
+    const ConvDilations& conv_dilations,
+    const InLeftPads& in_left_pads,
+    const InRightPads&,
+    const ConvTensorLayout layout = ConvTensorLayout::NCHW)
+{
+    using namespace ck;
+
+    constexpr auto I0 = Number<0>{};
+    constexpr auto I1 = Number<1>{};
+    auto f_kcyx       = [&](auto k, auto c, auto y, auto x) {
+        double v = 0;
+        for(int n = 0; n < out.mDesc.GetLengths()[0]; ++n)
+        {
+            for(int ho = 0; ho < out.mDesc.GetLengths()[2]; ++ho)
+            {
+                int hi = ho * conv_strides[I0] + y * conv_dilations[I0] - in_left_pads[I0];
+                for(int wo = 0; wo < out.mDesc.GetLengths()[3]; ++wo)
+                {
+                    int wi = wo * conv_strides[I1] + x * conv_dilations[I1] - in_left_pads[I1];
+                    if(hi >= 0 && hi < in.mDesc.GetLengths()[2] && wi >= 0 &&
+                       wi < in.mDesc.GetLengths()[3])
+                    {
+                        v += static_cast<const double>(in(n, c, hi, wi)) *
+                             static_cast<const double>(out(n, k, ho, wo));
+                    }
+                }
+            }
+        }
+        wei(k, c, y, x) = v;
+    };
+
+    auto f_kyxc = [&](auto k, auto y, auto x, auto c) {
+        double v = 0;
+        for(int n = 0; n < out.mDesc.GetLengths()[0]; ++n)
+        {
+            for(int ho = 0; ho < out.mDesc.GetLengths()[1]; ++ho)
+            {
+                int hi = ho * conv_strides[I0] + y * conv_dilations[I0] - in_left_pads[I0];
+                for(int wo = 0; wo < out.mDesc.GetLengths()[2]; ++wo)
+                {
+                    int wi = wo * conv_strides[I1] + x * conv_dilations[I1] - in_left_pads[I1];
+                    if(hi >= 0 && hi < in.mDesc.GetLengths()[1] && wi >= 0 &&
+                       wi < in.mDesc.GetLengths()[2])
+                    {
+                        v += static_cast<const double>(in(n, hi, wi, c)) *
+                             static_cast<const double>(out(n, ho, wo, k));
+                    }
+                }
+            }
+        }
+        wei(k, y, x, c) = v;
+    };
+
+    if(layout == ConvTensorLayout::NCHW)
+    {
+        make_ParallelTensorFunctor(f_kcyx,
+                                   wei.mDesc.GetLengths()[0],
+                                   wei.mDesc.GetLengths()[1],
+                                   wei.mDesc.GetLengths()[2],
+                                   wei.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
+    }
+    else if(layout == ConvTensorLayout::NHWC)
+    {
+        make_ParallelTensorFunctor(f_kyxc,
+                                   wei.mDesc.GetLengths()[0],
+                                   wei.mDesc.GetLengths()[1],
+                                   wei.mDesc.GetLengths()[2],
+                                   wei.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
+    }
+    else
+    {
+        throw std::runtime_error("wrong! not supported layout");
+    }
+}

host/host_tensor/include/host_gemm.hpp

@@ -0,0 +1,87 @@
+#pragma once
+#include "host_tensor.hpp"
+#include "gemm_common.hpp"
+
+template <typename AType, typename BType, typename CType>
+void host_gemm(const Tensor<AType>& a,
+               const Tensor<BType>& b,
+               Tensor<CType>& c,
+               const GemmMatrixLayout layout)
+{
+    if(layout == GemmMatrixLayout::MK_KN_MN)
+    {
+        auto f_mk_kn_mn = [&](auto m, auto n) {
+            const int K = a.mDesc.GetLengths()[1];
+
+            double v = 0;
+
+            for(int k = 0; k < K; ++k)
+            {
+                v += static_cast<const double>(a(m, k)) * static_cast<const double>(b(k, n));
+            }
+
+            c(m, n) = v;
+        };
+
+        make_ParallelTensorFunctor(f_mk_kn_mn, c.mDesc.GetLengths()[0], c.mDesc.GetLengths()[1])(
+            std::thread::hardware_concurrency());
+    }
+    else if(layout == GemmMatrixLayout::MK_NK_MN)
+    {
+        auto f_mk_nk_mn = [&](auto m, auto n) {
+            const int K = a.mDesc.GetLengths()[1];
+
+            double v = 0;
+
+            for(int k = 0; k < K; ++k)
+            {
+                v += static_cast<const double>(a(m, k)) * static_cast<const double>(b(n, k));
+            }
+
+            c(m, n) = v;
+        };
+
+        make_ParallelTensorFunctor(f_mk_nk_mn, c.mDesc.GetLengths()[0], c.mDesc.GetLengths()[1])(
+            std::thread::hardware_concurrency());
+    }
+    else if(layout == GemmMatrixLayout::KM_KN_MN)
+    {
+        auto f_km_kn_mn = [&](auto m, auto n) {
+            const int K = a.mDesc.GetLengths()[0];
+
+            double v = 0;
+
+            for(int k = 0; k < K; ++k)
+            {
+                v += static_cast<const double>(a(k, m)) * static_cast<const double>(b(k, n));
+            }
+
+            c(m, n) = v;
+        };
+
+        make_ParallelTensorFunctor(f_km_kn_mn, c.mDesc.GetLengths()[0], c.mDesc.GetLengths()[1])(
+            std::thread::hardware_concurrency());
+    }
+    else if(layout == GemmMatrixLayout::KM_NK_MN)
+    {
+        auto f_km_nk_mn = [&](auto m, auto n) {
+            const int K = a.mDesc.GetLengths()[0];
+
+            double v = 0;
+
+            for(int k = 0; k < K; ++k)
+            {
+                v += static_cast<const double>(a(k, m)) * static_cast<const double>(b(n, k));
+            }
+
+            c(m, n) = v;
+        };
+
+        make_ParallelTensorFunctor(f_km_nk_mn, c.mDesc.GetLengths()[0], c.mDesc.GetLengths()[1])(
+            std::thread::hardware_concurrency());
+    }
+    else
+    {
+        throw std::runtime_error("wrong! not supported layout");
+    }
+}

host/solver/include/conv_tunable_fwd_v4r4_xdlops_nchw_kcyx_nkhw.hpp

@@ -9,8 +9,8 @@ struct tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw
     int NPerBlock;
     int KPerBlock;
 
-    int MPerWave;
-    int NPerWave;
+    int MPerXDL;
+    int NPerXDL;
     int K1;
 
     int MRepeat;
@@ -45,8 +45,8 @@ static tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw
         128,                      // MPerBlock,
         128,                      // NPerBlock,
         4,                        // KPerBlock,
-        32,                       // MPerWave,
-        32,                       // NPerWave,
+        32,                       // MPerXDL,
+        32,                       // NPerXDL,
         4,                        // K1,
         2,                        // MRepeat,
         2,                        // NRepeat,

script/run.sh

@@ -12,13 +12,16 @@
 #export OLC_DEBUG_HIP_DUMP=1
 #export OLC_DEBUG_SAVE_TEMP_DIR=1
 
- make -j conv_fwd_driver_offline
- make -j conv_bwd_driver_offline
- make -j conv_fwd_driver_online
-
 #rm -rf /root/_hip_binary_kernels_/
 #rm -rf /tmp/olCompile*
 
+#make -j conv_fwd_driver_offline
+#make -j conv_bwd_driver_offline
+#make -j conv_wrw_driver_offline
+#make -j conv_fwd_driver_online
+
+ make -j gemm_driver_offline
+
 LAYOUT=$1
 ALGO=$2
 VERIFY=$3
@@ -30,7 +33,7 @@ REPEAT=$6
 #./host/driver_offline/conv_fwd_driver_offline  $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  128  128  192 3 3  71   71     2 2       1 1      1 1       1 1
 #./host/driver_offline/conv_fwd_driver_offline  $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  128  256  192 3 3  71   71     2 2       1 1      1 1       1 1
 #./host/driver_offline/conv_fwd_driver_offline  $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  128  256 1024 1 7  17   17     1 1       1 1      0 3       0 3
- ./host/driver_offline/conv_fwd_driver_offline  $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  256  256  256 3 3  14   14     1 1       1 1      1 1       1 1
+#./host/driver_offline/conv_fwd_driver_offline  $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  256  256  256 3 3  14   14     1 1       1 1      1 1       1 1
 #./host/driver_offline/conv_fwd_driver_offline  $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  128  128  128 3 3  14   14     1 1       1 1      1 1       1 1
 #./host/driver_offline/conv_fwd_driver_offline  $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  256  512  512 3 3   7    7     1 1       1 1      1 1       1 1
 
@@ -44,4 +47,12 @@ REPEAT=$6
 
 #./host/driver_offline/conv_bwd_driver_offline  $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  256  256  256 3 3  14   14     1 1       1 1      1 1       1 1
 
-#./host/driver_online/conv_fwd_driver_online    $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  128  256  192 3 3  71   71     2 2       1 1      1 1       1 1
+#./host/driver_offline/conv_wrw_driver_offline  $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  128  256  128 3 3  14   14     1 1       1 1      1 1       1 1
+
+#./host/driver_online/conv_fwd_driver_online    $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  128  128  192 3 3  71   71     2 2       1 1      1 1       1 1
+
+################################################ layout  algo  verify  init  log  repeat  M___ N___ K___
+#./host/driver_offline/gemm_driver_offline      $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT   960 1024 1024
+#./host/driver_offline/gemm_driver_offline      $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  1920 2048 2048
+ ./host/driver_offline/gemm_driver_offline      $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  3840 4096 4096
+#./host/driver_offline/gemm_driver_offline      $LAYOUT $ALGO $VERIFY $INIT $LOG $REPEAT  7680 8192 8192