Merge remote-tracking branch 'origin/develop' into gemm_f16_int8

CHANGELOG.md

@@ -11,7 +11,7 @@ None
 None
 
 ### Additions
-* Introduced wrapper sublibrary (limited functionality). (#1071, #1098, #1108, #1126)
+* Introduced wrapper sublibrary (limited functionality). (#1071, #1098, #1108, #1126, #1139)
 
 ### Changes
 None

docs/wrapper.rst

@@ -89,3 +89,4 @@ Operations
 -------------------------------------
 
 .. doxygenfile:: copy.hpp
+.. doxygenfile:: gemm.hpp

include/ck/wrapper/layout.hpp

@@ -248,6 +248,9 @@ struct Layout
     using DefaultIdxsTupleType = remove_cvref_t<decltype(GenerateDefaultIdxsTuple(Shape{}))>;
 
     public:
+    using LayoutShape                  = Shape;
+    using LayoutUnrolledDescriptorType = UnrolledDescriptorType;
+
     /**
      * \brief Transform descriptor to align to passed indexes.
      *

include/ck/wrapper/operations/copy.hpp

@@ -3,45 +3,18 @@
 
 #pragma once
 
-#include "../utils/tensor_utils.hpp"
+#include "ck/wrapper/utils/tensor_utils.hpp"
 
 #include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7.hpp"
 #include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v4r1.hpp"
 #include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v7.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_description/tensor_space_filling_curve.hpp"
 
 namespace ck {
 namespace wrapper {
 
-/**
- * \brief Perform generic copy between two tensors partitions (threadwise copy).
- *  Tensors must have the same size.
- *
- * \param src_tensor Source tensor.
- * \param dst_tensor Destination tensor.
- */
-template <typename SrcTensorType, typename DstTensorType>
-__host__ __device__ void copy(const SrcTensorType& src_tensor, DstTensorType& dst_tensor)
-{
-    if constexpr(!SrcTensorType::IsDynamicBuffer)
-    {
-        using SizeType = decltype(size(src_tensor));
-        static_for<0, SizeType{}, 1>{}([&](auto i) { dst_tensor(i) = src_tensor(i); });
-    }
-    else if constexpr(!DstTensorType::IsDynamicBuffer)
-    {
-        using SizeType = decltype(size(dst_tensor));
-        static_for<0, SizeType{}, 1>{}([&](auto i) { dst_tensor(i) = src_tensor(i); });
-    }
-    else
-    {
-        for(int i = 0; i < size(src_tensor); i++)
-        {
-            dst_tensor(i) = src_tensor(i);
-        }
-    }
-}
-
 /**
  * \brief Perform optimized copy between two tensors partitions (threadwise copy).
  * Tensors must have the same size.
@@ -167,9 +140,99 @@ __device__ void copy(const SrcTensorType& src_tensor, DstTensorType& dst_tensor)
     else
     {
         // Perform copy between StaticBuffers
-        copy(src_tensor, dst_tensor);
+        static_for<0, SrcShapeType::Size(), 1>{}([&](auto i) { dst_tensor(i) = src_tensor(i); });
     }
 }
 
+/**
+ * \brief Perform generic copy between two tensors partitions (threadwise copy).
+ *  Tensors must have the same size.
+ *
+ * \param src_tensor Source tensor.
+ * \param dst_tensor Destination tensor.
+ */
+template <typename SrcTensorType, typename DstTensorType>
+__host__ __device__ void copy(const SrcTensorType& src_tensor, DstTensorType& dst_tensor)
+{
+    // Generate default params
+    using SrcShapeType         = remove_cvref_t<decltype(shape(src_tensor))>;
+    constexpr index_t num_dims = SrcShapeType::Size();
+    // Incrementing dims 0, 1, 2 ... num_dims - 1
+    constexpr auto dim_access_order_tuple =
+        generate_tuple([](auto i) { return Number<i>{}; }, Number<num_dims>{});
+    constexpr index_t vector_dim        = num_dims - 1;
+    constexpr index_t scalar_per_vector = 1;
+    copy<decltype(dim_access_order_tuple), vector_dim, scalar_per_vector>(src_tensor, dst_tensor);
+}
+
+/**
+ * \brief Perform optimized blockwise copy between two tensors. Tensors must have the
+ *  same size.
+ *
+ * \note At now Vgpr and Sgpr are not supported.
+ *
+ * \tparam DimAccessOrderTuple Tuple with dimension access order.
+ * \tparam VectorDim Dimension for vectorize read and write.
+ * \tparam ScalarPerVector Number of scalar per vectorize read and write.
+ * \param src_tensor Source tensor.
+ * \param dst_tensor Destination tensor.
+ * \param thread_layout Thread layout per each dimension for copy.
+ */
+template <typename DimAccessOrderTuple,
+          index_t VectorDim,
+          index_t ScalarPerVector,
+          typename SrcTensorType,
+          typename DstTensorType,
+          typename ThreadLayoutTuple>
+__device__ void blockwise_copy(const SrcTensorType& src_tensor,
+                               DstTensorType& dst_tensor,
+                               [[maybe_unused]] ThreadLayoutTuple& thread_layout)
+{
+    static_assert(SrcTensorType::IsDynamicBuffer && DstTensorType::IsDynamicBuffer);
+    static_assert(is_detected<is_tuple, DimAccessOrderTuple>::value);
+
+    const auto& in_grid_desc  = layout(src_tensor).GetUnrolledDescriptor();
+    const auto& out_grid_desc = layout(dst_tensor).GetUnrolledDescriptor();
+
+    using SrcShapeType         = remove_cvref_t<decltype(shape(src_tensor))>;
+    constexpr index_t num_dims = SrcShapeType::Size();
+
+    constexpr auto tile_lengths_seq =
+        generate_sequence_v2([](auto I) { return size(SrcShapeType{}.At(I)); }, Number<num_dims>{});
+    constexpr auto thread_layout_seq = generate_sequence_v2(
+        [](auto I) { return size(ThreadLayoutTuple{}.At(I)); }, Number<num_dims>{});
+    constexpr auto dim_access_order = generate_sequence_v2(
+        [](auto I) { return DimAccessOrderTuple{}.At(I); }, Number<num_dims>{});
+
+    using ThisThreadBlock = ThisThreadBlock<size(ThreadLayoutTuple{})>;
+
+    // Perform copy between DynamicBuffers
+    auto transfer = ThreadGroupTensorSliceTransfer_v7<
+        ThisThreadBlock,
+        Tuple<typename SrcTensorType::TensorElementType>,
+        Tuple<typename DstTensorType::TensorElementType>,
+        decltype(tie(in_grid_desc)),
+        decltype(tie(out_grid_desc)),
+        tensor_operation::element_wise::PassThrough,
+        Sequence<static_cast<index_t>(InMemoryDataOperationEnum::Set)>,
+        std::remove_const_t<decltype(tile_lengths_seq)>,
+        std::remove_const_t<decltype(thread_layout_seq)>,
+        std::remove_const_t<decltype(dim_access_order)>,
+        std::remove_const_t<decltype(dim_access_order)>,
+        VectorDim,
+        ScalarPerVector,
+        Sequence<true>,
+        Sequence<true>>{in_grid_desc,
+                        make_tuple(src_tensor.GetMultiIdxOffsets()),
+                        out_grid_desc,
+                        make_tuple(dst_tensor.GetMultiIdxOffsets()),
+                        tensor_operation::element_wise::PassThrough{}};
+
+    transfer.Run(tie(in_grid_desc),
+                 tie(src_tensor.GetBuffer()),
+                 tie(out_grid_desc),
+                 tie(dst_tensor.GetBuffer()));
+}
+
 } // namespace wrapper
 } // namespace ck

include/ck/wrapper/operations/gemm.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/wrapper/utils/tensor_utils.hpp"
+#include "ck/wrapper/traits/blockwise_gemm_xdl_traits.hpp"
+
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+
+namespace ck {
+namespace wrapper {
+
+namespace {
+namespace detail {
+/**
+ * \brief Create block descriptor (K0, MPerBlock or NPerBlock, K1).
+ *
+ *
+ * \tparam K1 The number of K-dim elements that are packed together as a separate logical dimension.
+ * \tparam TileLayout Tensor data tile layout (M,K) or (N,K).
+ *
+ * \return Block descriptor (K0, MPerBlock or NPerBlock, K1)
+ */
+template <index_t K1, typename TileLayout>
+__device__ constexpr auto GetBlockDescriptor()
+{
+    using TileLayoutShape      = typename TileLayout::LayoutShape;
+    using TileLayoutDescriptor = typename TileLayout::LayoutUnrolledDescriptorType;
+
+    constexpr auto K0PerBlock = Number<size<1>(TileLayoutShape{})>{} / Number<K1>{};
+    // MPerBlock or NPerBlock
+    constexpr auto Dim0 = Number<size<0>(TileLayoutShape{})>{};
+
+    constexpr auto a_block_desc_k0_m_k1 = transform_tensor_descriptor(
+        TileLayoutDescriptor{},
+        make_tuple(make_unmerge_transform(make_tuple(K0PerBlock, Number<K1>{})),
+                   make_pass_through_transform(Dim0)),
+        make_tuple(Sequence<1>{}, Sequence<0>{}),
+        make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+    return a_block_desc_k0_m_k1;
+}
+
+} // namespace detail
+} // namespace
+
+/**
+ * \brief Perform blockwise gemm xdl on tensors stored in lds. Result will be
+ * stored in Vgpr register. A data layout must be (MPerBlock, KPerBlock) and B
+ * data layout must be (NPerBlock, KPerBlock).
+ *
+ * \note C output Vgpr register layout (8D):
+ * - MXdlPerWave - The number of MFMA instructions run by single wave in M
+ *                 dimension per tile.
+ * - NXdlPerWave - The number of MFMA instructions run by single wave in N
+ *                 dimension per tile.
+ * - MWave - Equals to 1 since this is for single wave.
+ * - NWave - Equals to 1 since this is for single wave.
+ * - NumGroupsPerBlock - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ * - NumInputsBlock - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ * - GroupSize - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ * - NumThreadsPerBlock - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ *
+ * \tparam DataType Input data types.
+ * \tparam BlockSize Tensor to pad.
+ * \tparam GemmTraits Traits of gemm xdl operation.
+ * \param a_local_tile_tensor A tensor in LDS memory for blockwise gemm
+ * (MPerBlock, KPerBlock) layout.
+ * \param b_local_tile_tensor B tensor in LDS memory for blockwise gemm
+ * (NPerBlock, KPerBlock) layout.
+ * \param c_reg_tensor C tensor VGPR memory for blockwise gemm.
+ */
+template <typename DataType,
+          index_t BlockSize,
+          typename GemmTraits,
+          typename ATensorType,
+          typename BTensorType,
+          typename CTensorType>
+__device__ void blockwise_gemm_xdl(const ATensorType& a_local_tile_tensor,
+                                   const BTensorType& b_local_tile_tensor,
+                                   CTensorType& c_reg_tensor)
+{
+    static_assert(ATensorType::TensorBufferAddressSpace == MemoryTypeEnum::Lds);
+    static_assert(BTensorType::TensorBufferAddressSpace == MemoryTypeEnum::Lds);
+    static_assert(CTensorType::TensorBufferAddressSpace == MemoryTypeEnum::Vgpr);
+    static_assert(is_same_v<DataType, typename ATensorType::TensorElementType>);
+    static_assert(is_same_v<DataType, typename BTensorType::TensorElementType>);
+
+    constexpr bool is_integer =
+        is_same_v<DataType, int8_t> || is_same_v<DataType, int16_t> || is_same_v<DataType, int32_t>;
+    using GemmAccDataType = std::conditional_t<is_integer, int32_t, float>;
+
+    using ATileLayout = remove_cvref_t<decltype(layout(a_local_tile_tensor))>;
+    using BTileLayout = remove_cvref_t<decltype(layout(b_local_tile_tensor))>;
+
+    using ABlockDesc_K0_M_K1_Type =
+        decltype(detail::GetBlockDescriptor<GemmTraits::K1, ATileLayout>());
+    using BBlockDesc_K0_N_K1_Type =
+        decltype(detail::GetBlockDescriptor<GemmTraits::K1, BTileLayout>());
+
+    BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
+                                                        DataType,
+                                                        DataType,
+                                                        GemmAccDataType,
+                                                        ABlockDesc_K0_M_K1_Type,
+                                                        BBlockDesc_K0_N_K1_Type,
+                                                        GemmTraits::MPerXDL,
+                                                        GemmTraits::NPerXDL,
+                                                        GemmTraits::MXdlPerWave,
+                                                        GemmTraits::NXdlPerWave,
+                                                        GemmTraits::K1>
+        blockwise_gemm_xdl_op{};
+
+    blockwise_gemm_xdl_op.Run(
+        a_local_tile_tensor.GetBuffer(), b_local_tile_tensor.GetBuffer(), c_reg_tensor.GetBuffer());
+}
+
+/**
+ * \brief Create local partition per thread for C tensor.
+ *
+ * \note C output global memory layout (8D):
+ * - MXdlPerWave - The number of MFMA instructions run by single wave in M
+ *                 dimension.
+ * - NXdlPerWave - The number of MFMA instructions run by single wave in N
+ *                 dimension.
+ * - MWave - The number of waves in single tile M dimension per tile.
+ * - NWave - The number of waves in single tile N dimension per tile.
+ * - NumGroupsPerBlock - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ * - NumInputsBlock - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ * - GroupSize - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ * - NumThreadsPerBlock - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ *
+ * \tparam DataType Input data types.
+ * \tparam ATileLayout A tensor layout.
+ * \tparam BTileLayout B tensor layout.
+ * \tparam BlockSize Number of threads in block.
+ * \tparam GemmTraits Traits of gemm xdl operation.
+ * \param c_local_tile_tensor C tensor in LDS memory for blockwise gemm
+ * (MPerBlock, NPerBlock) layout.
+ *
+ * \return Partition c tensor for blockwise gemm.
+ */
+template <typename DataType,
+          typename ATileLayout,
+          typename BTileLayout,
+          index_t BlockSize,
+          typename GemmTraits,
+          typename CTensorType>
+__host__ __device__ constexpr auto
+make_blockwise_gemm_xdl_c_local_partition(CTensorType& c_local_tile_tensor)
+{
+    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 bool is_integer =
+        is_same_v<DataType, int8_t> || is_same_v<DataType, int16_t> || is_same_v<DataType, int32_t>;
+    using GemmAccDataType = std::conditional_t<is_integer, int32_t, float>;
+
+    using ABlockDesc_K0_M_K1_Type =
+        decltype(detail::GetBlockDescriptor<GemmTraits::K1, ATileLayout>());
+    using BBlockDesc_K0_N_K1_Type =
+        decltype(detail::GetBlockDescriptor<GemmTraits::K1, BTileLayout>());
+
+    using BlockwiseGemmXdlops =
+        BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
+                                                            DataType,
+                                                            DataType,
+                                                            GemmAccDataType,
+                                                            ABlockDesc_K0_M_K1_Type,
+                                                            BBlockDesc_K0_N_K1_Type,
+                                                            GemmTraits::MPerXDL,
+                                                            GemmTraits::NPerXDL,
+                                                            GemmTraits::MXdlPerWave,
+                                                            GemmTraits::NXdlPerWave,
+                                                            GemmTraits::K1>;
+
+    constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
+        BlockwiseGemmXdlops::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+    constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I0);
+    constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I1);
+    constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I2);
+    constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I3);
+    constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I4);
+    constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I5);
+    constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I6);
+    constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2.GetLength(I7);
+
+    // Calculate offset on grid
+    const auto c_thread_mtx_on_block =
+        BlockwiseGemmXdlops::CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
+
+    const index_t m_thread_data_on_grid =
+        c_local_tile_tensor.GetMultiIdxOffsets()[I0] + c_thread_mtx_on_block[I0];
+
+    const index_t n_thread_data_on_grid =
+        c_local_tile_tensor.GetMultiIdxOffsets()[I1] + c_thread_mtx_on_block[I1];
+
+    const auto m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor = make_single_stage_tensor_adaptor(
+        make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
+        make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+        make_tuple(Sequence<0>{}));
+
+    const auto m_thread_data_on_grid_idx =
+        m_thread_data_on_grid_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
+            make_multi_index(m_thread_data_on_grid));
+
+    const auto n_thread_data_on_grid_to_n0_n1_n2_adaptor =
+        make_single_stage_tensor_adaptor(make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
+                                         make_tuple(Sequence<0, 1, 2>{}),
+                                         make_tuple(Sequence<0>{}));
+
+    const auto n_thread_data_on_grid_idx =
+        n_thread_data_on_grid_to_n0_n1_n2_adaptor.CalculateBottomIndex(
+            make_multi_index(n_thread_data_on_grid));
+    // Create partition shape based on descriptor dims.
+    const auto partition_shape = make_tuple(M0, N0, I1, I1, M2, I1, M4, I1);
+
+    const auto partition_desc = BlockwiseGemmXdlops::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(
+        layout(c_local_tile_tensor).GetUnrolledDescriptor());
+    const auto partition_layout =
+        Layout<remove_reference_t<decltype(partition_shape)>, decltype(partition_desc)>(
+            partition_shape, partition_desc);
+    auto partition_tensor = make_tensor<CTensorType::TensorBufferAddressSpace>(
+        c_local_tile_tensor.GetPointer(), partition_layout);
+    partition_tensor.SetMultiIdxOffset(make_multi_index(m_thread_data_on_grid_idx[I0],
+                                                        n_thread_data_on_grid_idx[I0],
+                                                        m_thread_data_on_grid_idx[I1],
+                                                        n_thread_data_on_grid_idx[I1],
+                                                        m_thread_data_on_grid_idx[I2],
+                                                        m_thread_data_on_grid_idx[I3],
+                                                        m_thread_data_on_grid_idx[I4],
+                                                        n_thread_data_on_grid_idx[I2]));
+    return partition_tensor;
+}
+
+/**
+ * \brief Create local partition per thread for C tensor.
+ *
+ * \note C output Vgpr register layout (8D):
+ * - MXdlPerWave - The number of MFMA instructions run by single wave in M
+ *                 dimension per tile.
+ * - NXdlPerWave - The number of MFMA instructions run by single wave in N
+ *                 dimension per tile.
+ * - MWave - Equals to 1 since this is for single wave.
+ * - NWave - Equals to 1 since this is for single wave.
+ * - NumGroupsPerBlock - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ * - NumInputsBlock - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ * - GroupSize - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ * - NumThreadsPerBlock - Mfma instruction internal layout (depeneds on the
+ *                       instruction size).
+ *
+ * \tparam DataType Input data types.
+ * \tparam ATileLayout A tensor layout.
+ * \tparam BTileLayout B tensor layout.
+ * \tparam BlockSize Number of threads in block.
+ * \tparam GemmTraits Traits of gemm xdl operation.
+ *
+ * \return Vgpr c tensor for blockwise gemm.
+ */
+template <typename DataType,
+          typename ATileLayout,
+          typename BTileLayout,
+          index_t BlockSize,
+          typename GemmTraits>
+__host__ __device__ constexpr auto make_blockwise_gemm_xdl_c_vgpr()
+{
+    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 bool is_integer =
+        is_same_v<DataType, int8_t> || is_same_v<DataType, int16_t> || is_same_v<DataType, int32_t>;
+    using GemmAccDataType = std::conditional_t<is_integer, int32_t, float>;
+
+    using ABlockDesc_K0_M_K1_Type =
+        decltype(detail::GetBlockDescriptor<GemmTraits::K1, ATileLayout>());
+    using BBlockDesc_K0_N_K1_Type =
+        decltype(detail::GetBlockDescriptor<GemmTraits::K1, BTileLayout>());
+
+    using BlockwiseGemmXdlops =
+        BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
+                                                            DataType,
+                                                            DataType,
+                                                            GemmAccDataType,
+                                                            ABlockDesc_K0_M_K1_Type,
+                                                            BBlockDesc_K0_N_K1_Type,
+                                                            GemmTraits::MPerXDL,
+                                                            GemmTraits::NPerXDL,
+                                                            GemmTraits::MXdlPerWave,
+                                                            GemmTraits::NXdlPerWave,
+                                                            GemmTraits::K1>;
+    // Calcualte descriptor, shape and layout
+    constexpr auto vgpr_desc = BlockwiseGemmXdlops::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+    const auto vgpr_shape    = make_tuple(vgpr_desc.GetLengths()[I0],
+                                       vgpr_desc.GetLengths()[I1],
+                                       vgpr_desc.GetLengths()[I2],
+                                       vgpr_desc.GetLengths()[I3],
+                                       vgpr_desc.GetLengths()[I4],
+                                       vgpr_desc.GetLengths()[I5],
+                                       vgpr_desc.GetLengths()[I6],
+                                       vgpr_desc.GetLengths()[I7]);
+    const auto vgpr_layout = Layout<remove_reference_t<decltype(vgpr_shape)>, decltype(vgpr_desc)>(
+        vgpr_shape, vgpr_desc);
+    // Get vector type for Vgpr
+    using BlockwiseGemmCThreadBufferType =
+        remove_reference_t<decltype(BlockwiseGemmXdlops{}.GetCThreadBuffer())>;
+    using VgprVectorType = typename BlockwiseGemmCThreadBufferType::V;
+    return ck::wrapper::make_register_tensor<ck::wrapper::MemoryTypeEnum::Vgpr, VgprVectorType>(
+        vgpr_layout);
+}
+
+} // namespace wrapper
+} // namespace ck

include/ck/wrapper/tensor.hpp

@@ -10,8 +10,8 @@
 namespace ck {
 namespace wrapper {
 
-namespace detail {
 namespace {
+namespace detail {
 /**
  * \brief Check if Tuple contains Slice object
  *
@@ -187,8 +187,8 @@ __host__ __device__ constexpr auto GenerateSlicedDescriptor(const Tuple<Ts...>&
     const auto upper_dims = decltype(GenerateUpperDims<0>(TransformsTupleType{})){};
     return transform_tensor_descriptor(flatten_desc, transforms, lower_dims, upper_dims);
 }
-} // namespace
 } // namespace detail
+} // namespace
 
 /**
  * \brief Tensor wrapper that performs static and dynamic buffer logic.
@@ -209,7 +209,10 @@ struct Tensor
     public:
     using ElementSpaceSize  = decltype(Layout<Shape, UnrolledDescriptorType>{
         Shape{}, UnrolledDescriptorType{}}.GetElementSpaceSize()); // SpaceSize type for buffer
-    using TensorElementType = ElementType;                          // DataType
+    using TensorElementType = std::conditional_t<
+        is_scalar_type<ElementType>::value,
+        ElementType,
+        typename scalar_type<std::remove_const_t<ElementType>>::type>; // DataType
 
     static constexpr MemoryTypeEnum TensorBufferAddressSpace = BufferAddressSpace;
     static constexpr bool IsDynamicBuffer = !(BufferAddressSpace == MemoryTypeEnum ::Sgpr ||
@@ -280,7 +283,7 @@ struct Tensor
      * \return Requested value.
      */
     template <typename... Ts, enable_if_t<!detail::HasSlice(Tuple<Ts...>{}), bool> = false>
-    __host__ __device__ const ElementType& operator[](const Tuple<Ts...>& idx) const
+    __host__ __device__ const TensorElementType& operator[](const Tuple<Ts...>& idx) const
     {
         if constexpr(IsDynamicBuffer)
         {
@@ -301,13 +304,13 @@ struct Tensor
     }
 
     template <typename... Ts, enable_if_t<!detail::HasSlice(Tuple<Ts...>{}), bool> = false>
-    __host__ __device__ const ElementType& operator()(const Tuple<Ts...>& idx) const
+    __host__ __device__ const TensorElementType& operator()(const Tuple<Ts...>& idx) const
     {
         return this->operator[](idx);
     }
 
     template <typename... Idxs, enable_if_t<!detail::HasSlice(Tuple<Idxs...>{}), bool> = false>
-    __host__ __device__ const ElementType& operator()(Idxs... idxs) const
+    __host__ __device__ const TensorElementType& operator()(Idxs... idxs) const
     {
         return this->operator[](make_tuple(idxs...));
     }
@@ -319,7 +322,7 @@ struct Tensor
      * \return Requested value.
      */
     template <typename... Ts, enable_if_t<!detail::HasSlice(Tuple<Ts...>{}), bool> = false>
-    __host__ __device__ ElementType& operator[](const Tuple<Ts...>& idx)
+    __host__ __device__ TensorElementType& operator[](const Tuple<Ts...>& idx)
     {
         if constexpr(IsDynamicBuffer)
         {
@@ -340,13 +343,13 @@ struct Tensor
     }
 
     template <typename... Ts, enable_if_t<!detail::HasSlice(Tuple<Ts...>{}), bool> = false>
-    __host__ __device__ ElementType& operator()(const Tuple<Ts...>& idx)
+    __host__ __device__ TensorElementType& operator()(const Tuple<Ts...>& idx)
     {
         return this->operator[](idx);
     }
 
     template <typename... Idxs, enable_if_t<!detail::HasSlice(Tuple<Idxs...>{}), bool> = false>
-    __host__ __device__ ElementType& operator()(Idxs... idxs)
+    __host__ __device__ TensorElementType& operator()(Idxs... idxs)
     {
         return this->operator[](make_tuple(idxs...));
     }
@@ -366,7 +369,7 @@ struct Tensor
      *
      * \return Pointer.
      */
-    __host__ __device__ ElementType* GetPointer() const { return buffer_.p_data_; }
+    __host__ __device__ TensorElementType* GetPointer() const { return buffer_.p_data_; }
 
     __host__ __device__ constexpr auto& GetBuffer() { return buffer_; }
     __host__ __device__ constexpr auto& GetBuffer() const { return buffer_; }
@@ -395,10 +398,18 @@ struct Tensor
                                             ElementType,
                                             ElementSpaceSize,
                                             true /*InvalidElementUseNumericalZeroValue*/>;
-    using StaticBufferType  = StaticBuffer<BufferAddressSpace,
-                                          ElementType,
-                                          size(Shape{}),
-                                          true /*InvalidElementUseNumericalZeroValue*/>;
+    using StaticBufferType  = std::conditional_t<
+        is_scalar_type<ElementType>::value,
+        StaticBuffer<BufferAddressSpace,
+                     ElementType,
+                     size(Shape{}),
+                     true /*InvalidElementUseNumericalZeroValue*/>,
+        StaticBufferTupleOfVector<BufferAddressSpace,
+                                  TensorElementType,
+                                  size(Shape{}) /
+                                      scalar_type<std::remove_const_t<ElementType>>::vector_size,
+                                  scalar_type<std::remove_const_t<ElementType>>::vector_size,
+                                  true /*InvalidElementUseNumericalZeroValue*/>>;
     // If register use static buffer, else use dynamic buffer
     using Buffer = std::conditional_t<IsDynamicBuffer, DynamicBufferType, StaticBufferType>;
 

include/ck/wrapper/traits/blockwise_gemm_xdl_traits.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/ck.hpp"
+
+namespace ck {
+namespace wrapper {
+
+/**
+ * \brief Traits for blockwise gemm xdl.
+ *
+ * \tparam MPerXDLValue The MFMA instruction size in M dimension.
+ * \tparam NPerXDLValue The MFMA instruction size in N dimension.
+ * \tparam MXdlPerWaveValue  The number of MFMA instructions run by single
+ * wave in M dimension.
+ * \tparam NXdlPerWaveValue  The number of MFMA instructions run by single
+ * wave in N dimension.
+ * \tparam K1Value The number of K-dim elements that are packed together as
+ * a separate logical dimension. Usually aligns with vector load size.
+ */
+template <index_t MPerXDLValue,
+          index_t NPerXDLValue,
+          index_t MXdlPerWaveValue,
+          index_t NXdlPerWaveValue,
+          index_t K1Value>
+struct BlockwisGemmXdlTraits
+{
+    static constexpr index_t MPerXDL     = MPerXDLValue;
+    static constexpr index_t NPerXDL     = NPerXDLValue;
+    static constexpr index_t MXdlPerWave = MXdlPerWaveValue;
+    static constexpr index_t NXdlPerWave = NXdlPerWaveValue;
+    static constexpr index_t K1          = K1Value;
+};
+
+struct BlockwisGemmXdlTraits_32x32Xdl_4x2XdlPerWave_4K1 : BlockwisGemmXdlTraits<32, 32, 4, 2, 4>
+{
+};
+struct BlockwisGemmXdlTraits_32x32Xdl_2x4XdlPerWave_4K1 : BlockwisGemmXdlTraits<32, 32, 2, 4, 4>
+{
+};
+struct BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1 : BlockwisGemmXdlTraits<32, 32, 2, 2, 4>
+{
+};
+
+} // namespace wrapper
+} // namespace ck

include/ck/wrapper/utils/tensor_partition.hpp

@@ -6,6 +6,7 @@
 #include "tensor_utils.hpp"
 #include "layout_utils.hpp"
 
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
 #include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
 #include "ck/tensor_description/cluster_descriptor.hpp"
 
@@ -14,6 +15,8 @@ namespace wrapper {
 
 namespace {
 
+namespace detail {
+
 /**
  * \brief Calculate shape for partition based on number of threads per each dim and
  * previous shape
@@ -30,26 +33,109 @@ __host__ __device__ constexpr auto CalculateLocalPartitionShape(const Tuple<Ts..
     return generate_tuple(
         [&](auto i) {
             constexpr auto num_i = Number<i>{};
-            const auto slice_len = size<num_i>(shape) / thread_lengths.At(num_i);
+            const auto slice_len =
+                ck::math::integer_divide_ceil(size<num_i>(shape), thread_lengths.At(num_i));
             return slice_len;
         },
         Number<Tuple<Ls...>::Size()>{});
 }
 
+/**
+ * \brief Apply projection.
+ *
+ * \param base_tuple Tuple to apply projection.
+ * \param projection Projection to remove selected dim from partitioning.
+ * slice(X) to remove, where X is dim size, Number<1>{} to keep.
+ * \return Multi index after projection.
+ */
+template <typename MultiIndex, typename ProjectionTuple>
+__host__ __device__ constexpr auto
+ApplyProjection([[maybe_unused]] const MultiIndex& base_tuple,
+                [[maybe_unused]] const ProjectionTuple& projection)
+{
+    if constexpr(is_same_v<ProjectionTuple, Tuple<>>)
+    {
+        return Tuple<>{};
+    }
+    else
+    {
+        auto base_tuple_after_projection = generate_tuple(
+            [&](auto i) {
+                const auto i_num = Number<i.value>{};
+                static_assert(
+                    is_detected<is_slice, tuple_element_t<i_num, ProjectionTuple>>::value ||
+                    is_same_v<tuple_element_t<i_num, ProjectionTuple>, Number<1>>);
+                if constexpr(is_detected<is_slice, tuple_element_t<i_num, ProjectionTuple>>::value)
+                {
+                    // When slice (to remove), then insert empty tuple (will be removed in next
+                    // step).
+                    return Tuple<>{};
+                }
+                else
+                {
+                    return base_tuple.At(i_num);
+                }
+            },
+            Number<MultiIndex::Size()>{});
+        // Remove empty tuples
+        return UnrollNestedTuple<0, 1>(base_tuple_after_projection);
+    }
+}
+
+/**
+ * \brief Calculate shape with dims from projection.
+ *
+ * \param shape Base tensor shape.
+ * \param projection Projection to remove selected dim from partitioning.
+ * slice(X) to remove, where X is dim size, Number<1>{} to keep.
+ * \return Shape with dims from projection
+ */
+template <typename... Ts, typename... Ps>
+__host__ __device__ constexpr auto CalculateShapeWithProjection(const Tuple<Ts...>& shape,
+                                                                const Tuple<Ps...>& projection)
+{
+    return generate_tuple(
+        [&](auto i) {
+            if constexpr(is_detected<is_slice, tuple_element_t<i, Tuple<Ps...>>>::value)
+            {
+                return size<i>(projection).to_;
+            }
+            else
+            {
+                // number of shape element in actual fragment of shape and projection (method to
+                // calculate shape idx)
+                constexpr index_t shape_i =
+                    detail::ApplyProjection(TupleSlice<0, i>(Tuple<Ts...>{}),
+                                            TupleSlice<0, i>(Tuple<Ps...>{}))
+                        .Size();
+                return size<shape_i>(shape);
+            }
+        },
+        Number<Tuple<Ps...>::Size()>{});
+}
+
 /**
  * \brief Calculate total number of blocks.
  *
  * \param shape Base tensor shape.
  * \param tile_shape Tile shape.
+ * \param projection Projection is used to remove selected dim from
+ * partitioning. Use `slice(X)` to remove dimension, where X is dim
+ * size. Use `Number<1>{}` to keep it.
  * \return Tuple with blocks number.
  */
-template <typename... Ts, typename... Ls>
+template <typename... Ts, typename... Ls, typename... Ps>
 __host__ __device__ constexpr auto CalculateGridSize(const Tuple<Ts...>& shape,
-                                                     const Tuple<Ls...>& tile_shape)
+                                                     const Tuple<Ls...>& tile_shape,
+                                                     const Tuple<Ps...>& projection)
 {
-    static_assert(Tuple<Ts...>::Size() == Tuple<Ls...>::Size(), "Wrong thread_lengths shape.");
-    return generate_tuple([&](auto i) { return size<i>(shape) / size<i>(tile_shape); },
-                          Number<Tuple<Ls...>::Size()>{});
+    auto shape_with_projection = CalculateShapeWithProjection(shape, projection);
+    return generate_tuple(
+        [&](auto i) {
+            return ck::math::integer_divide_ceil(size<i>(shape_with_projection),
+                                                 size<i>(tile_shape));
+        },
+        Number<Tuple<Ls...>::Size()>{});
 }
 
 /**
@@ -69,6 +155,20 @@ CalculateOffsetMultiIdxs(const ThreadIdxs& thread_idxs,
     return thread_idxs * partition_lengths_seq + old_offset_idxs;
 }
 
+/**
+ * \brief Calculate default projection.
+ *
+ * \param tile_shape Tile shape.
+ * \return Default projection (filled with Number<1>{}).
+ */
+template <typename TileShape>
+__host__ __device__ constexpr auto
+GenerateDefaultProjection([[maybe_unused]] const TileShape tile_shape)
+{
+    return generate_tuple([&](auto) { return Number<1>{}; }, Number<TileShape::Size()>{});
+}
+
+} // namespace detail
 } // namespace
 
 /**
@@ -78,35 +178,45 @@ CalculateOffsetMultiIdxs(const ThreadIdxs& thread_idxs,
  * \param tensor Tensor for partition.
  * \param thread_lengths Layout of threads (could not be nested).
  * \param thread_id Thread index represented as integer.
+ * \param projection Projection is used to remove selected dim from
+ * partitioning. Use `slice(X)` to remove dimension, where X is dim
+ * size. Use `Number<1>{}` to keep it.
  * \return Partition tensor.
  */
-template <typename TensorType, typename ThreadLengthsTuple>
+template <typename TensorType, typename ThreadLengthsTuple, typename ProjectionTuple>
 __host__ __device__ constexpr auto
 make_local_partition(TensorType& tensor,
                      [[maybe_unused]] const ThreadLengthsTuple& thread_lengths,
-                     const index_t thread_id)
+                     const index_t thread_id,
+                     const ProjectionTuple& projection)
 {
     static_assert(!IsNestedTuple(ThreadLengthsTuple{}));
     // Calculate new partition shape
     const auto& tensor_shape = shape(tensor);
+    // Calculate projected thread lengths
+    constexpr auto projected_thread_lengths =
+        detail::ApplyProjection(ThreadLengthsTuple{}, ProjectionTuple{});
     constexpr auto partition_shape =
-        CalculateLocalPartitionShape(decltype(tensor_shape){}, ThreadLengthsTuple{});
+        detail::CalculateLocalPartitionShape(decltype(tensor_shape){}, projected_thread_lengths);
     // Create Thread Cluster Descriptor
-    constexpr auto partition_lengths_seq = generate_sequence_v2(
-        [&](auto I) { return size<I>(partition_shape); }, Number<ThreadLengthsTuple::Size()>{});
+    constexpr auto partition_shape_seq =
+        generate_sequence_v2([&](auto I) { return size<I>(partition_shape); },
+                             Number<decltype(partition_shape)::Size()>{});
     constexpr auto thread_lengths_seq =
         generate_sequence_v2([&](auto I) { return size<I>(ThreadLengthsTuple{}); },
                              Number<ThreadLengthsTuple::Size()>{});
     constexpr auto thread_cluster_desc_ = make_cluster_descriptor(thread_lengths_seq);
     // Calculate thread idxs and offsets
     const auto thread_idxs = thread_cluster_desc_.CalculateBottomIndex(make_multi_index(thread_id));
-    const auto offset_multi_idxs =
-        CalculateOffsetMultiIdxs(thread_idxs, partition_lengths_seq, tensor.GetMultiIdxOffsets());
+    // Apply projection on thread idxs to remove not needed idxs
+    const auto projected_thread_idxs = detail::ApplyProjection(thread_idxs, projection);
+    const auto offset_multi_idxs     = detail::CalculateOffsetMultiIdxs(
+        projected_thread_idxs, partition_shape_seq, tensor.GetMultiIdxOffsets());
     // Create new layout and tensor
-    auto& flatten_desc = layout(tensor).GetUnrolledDescriptor();
+    auto& unrolled_desc = layout(tensor).GetUnrolledDescriptor();
     const auto partition_layout =
-        Layout<remove_reference_t<decltype(partition_shape)>, decltype(flatten_desc)>(
-            partition_shape, flatten_desc);
+        Layout<remove_reference_t<decltype(partition_shape)>, decltype(unrolled_desc)>(
+            partition_shape, unrolled_desc);
     auto partition_tensor =
         make_tensor<TensorType::TensorBufferAddressSpace>(tensor.GetPointer(), partition_layout);
     // Apply offsets
@@ -114,6 +224,24 @@ make_local_partition(TensorType& tensor,
     return partition_tensor;
 }
 
+/**
+ * \brief Create local partition for thread (At now only packed partition
+ * is supported).
+ *
+ * \param tensor Tensor for partition.
+ * \param thread_lengths Layout of threads (could not be nested).
+ * \param thread_id Thread index represented as integer.
+ * \return Partition tensor.
+ */
+template <typename TensorType, typename ThreadLengthsTuple>
+__host__ __device__ constexpr auto make_local_partition(TensorType& tensor,
+                                                        const ThreadLengthsTuple& thread_lengths,
+                                                        const index_t thread_id)
+{
+    const auto projection = detail::GenerateDefaultProjection(ThreadLengthsTuple{});
+    return make_local_partition(tensor, thread_lengths, thread_id, projection);
+}
+
 /**
  * \brief Create local tile for thread block. (At now only packed tile
  * is supported).
@@ -125,22 +253,29 @@ make_local_partition(TensorType& tensor,
  * \param tensor Tensor for partition.
  * \param tile_shape Shapes of requested tile.
  * \param block_id Block index represented as integer.
-
+ * \param projection Projection to remove selected dim from partitioning.
+ * slice(X) to remove, where X is dim size, Number<1>{} to keep.
  * \return Tile tensor.
  */
-template <typename TensorType, typename BlockShapeTuple>
-__host__ __device__ constexpr auto
-make_local_tile(const TensorType& tensor, const BlockShapeTuple& tile_shape, const index_t block_id)
+template <typename TensorType, typename BlockShapeTuple, typename ProjectionTuple>
+__host__ __device__ constexpr auto make_local_tile(const TensorType& tensor,
+                                                   const BlockShapeTuple& tile_shape,
+                                                   const index_t block_id,
+                                                   const ProjectionTuple& projection)
 {
     static_assert(!IsNestedTuple(BlockShapeTuple{}));
 
+    constexpr bool is_default_projection =
+        is_same_v<ProjectionTuple, decltype(detail::GenerateDefaultProjection(BlockShapeTuple{}))>;
+
     constexpr auto I0 = Number<0>{};
     constexpr auto I1 = Number<1>{};
     constexpr auto I2 = Number<2>{};
 
     auto& aligned_desc = layout(tensor).GetMergedNestingDescriptor();
 
-    if constexpr(BlockShapeTuple::Size() == I2)
+    // TODO: Enable block_2_tile_map partitioning for non-default projection.
+    if constexpr(BlockShapeTuple::Size() == I2 && is_default_projection)
     {
         // Optimized version for 2d tile shape [MxK]
         const auto block_2_tile_map =
@@ -169,20 +304,24 @@ make_local_tile(const TensorType& tensor, const BlockShapeTuple& tile_shape, con
     {
         // Calculate offsets
         // Sequence with data to process per block
-        constexpr auto tile_shape_seq =
-            generate_sequence_v2([](auto I) { return size(BlockShapeTuple{}.At(I)); },
-                                 Number<BlockShapeTuple::Size()>{});
+        constexpr auto projected_tile_shape =
+            detail::ApplyProjection(BlockShapeTuple{}, ProjectionTuple{});
+        using ProjectedTileShapeTuple = decltype(projected_tile_shape);
+        constexpr auto projected_tile_shape_seq =
+            generate_sequence_v2([](auto I) { return ProjectedTileShapeTuple{}.At(I); },
+                                 Number<ProjectedTileShapeTuple::Size()>{});
         // Tuple with number of blocks
-        const auto block_lengths           = CalculateGridSize(shape(tensor), tile_shape);
-        constexpr auto block_cluster_desc_ = make_cluster_descriptor(block_lengths);
+        const auto block_lengths = detail::CalculateGridSize(shape(tensor), tile_shape, projection);
+        const auto block_cluster_desc_ = make_cluster_descriptor(block_lengths);
         const auto block_idxs =
             block_cluster_desc_.CalculateBottomIndex(make_multi_index(block_id));
-        const auto offset_multi_idxs =
-            CalculateOffsetMultiIdxs(block_idxs, tile_shape_seq, tensor.GetMultiIdxOffsets());
+        const auto projected_block_idxs = detail::ApplyProjection(block_idxs, projection);
+        const auto offset_multi_idxs    = detail::CalculateOffsetMultiIdxs(
+            projected_block_idxs, projected_tile_shape_seq, tensor.GetMultiIdxOffsets());
         // Create new layout and tensor
         const auto tile_layout =
-            Layout<remove_reference_t<decltype(tile_shape)>, decltype(aligned_desc)>(tile_shape,
-                                                                                     aligned_desc);
+            Layout<remove_reference_t<ProjectedTileShapeTuple>, decltype(aligned_desc)>(
+                projected_tile_shape, aligned_desc);
         auto tile_tensor =
             make_tensor<TensorType::TensorBufferAddressSpace>(tensor.GetPointer(), tile_layout);
         // Apply offsets
@@ -191,5 +330,61 @@ make_local_tile(const TensorType& tensor, const BlockShapeTuple& tile_shape, con
     }
 }
 
+/**
+ * \brief Create local tile for thread block. (At now only packed tile
+ * is supported).
+ *
+ * \note Currently to get the best performance please use 2d shape.
+ *
+ * \param tensor Tensor for partition.
+ * \param tile_shape Shapes of requested tile.
+ * \param block_id Block index represented as integer.
+ * \return Tile tensor.
+ */
+template <typename TensorType, typename BlockShapeTuple>
+__host__ __device__ constexpr auto
+make_local_tile(const TensorType& tensor, const BlockShapeTuple& tile_shape, const index_t block_id)
+{
+    const auto projection = detail::GenerateDefaultProjection(BlockShapeTuple{});
+    return make_local_tile(tensor, tile_shape, block_id, projection);
+}
+
+/**
+ * \brief Pad tensor shapes to be adjusted to tile lengths.
+ *
+ *
+ * \param tensor Tensor to pad.
+ * \param tile_lengths Tile lengths to align tensor shape.
+ * \return Padded tensor.
+ */
+template <typename TensorType, typename TileLengths>
+__host__ __device__ constexpr auto pad(const TensorType& tensor, const TileLengths& tile_lengths)
+{
+    const auto& tensor_shape = shape(tensor);
+    using TensorShapeType    = remove_reference_t<decltype(tensor_shape)>;
+    auto& unrolled_desc      = layout(tensor).GetUnrolledDescriptor();
+    // Generate sequence with ones to mark that all dims will be padded
+    constexpr auto do_pads_seq =
+        generate_sequence_v2([](auto) { return Number<1>{}; }, Number<TensorShapeType::Size()>{});
+    // Create descriptor with padding
+    auto padded_desc =
+        tensor_operation::device::PadTensorDescriptor(unrolled_desc, tile_lengths, do_pads_seq);
+    // Generate padded shape
+    const auto padded_shape = generate_tuple(
+        [&](auto i) {
+            const auto& dim         = size<i>(tensor_shape);
+            const auto& tile_length = size<i>(tile_lengths);
+            return ck::math::integer_divide_ceil(dim, tile_length) * tile_length;
+        },
+        Number<TileLengths::Size()>{});
+    // Create layout and tensor
+    const auto padded_layout =
+        Layout<decltype(padded_shape), decltype(padded_desc)>(padded_shape, padded_desc);
+    auto partition_tensor =
+        make_tensor<TensorType::TensorBufferAddressSpace>(tensor.GetPointer(), padded_layout);
+    partition_tensor.SetMultiIdxOffset(tensor.GetMultiIdxOffsets());
+    return partition_tensor;
+}
+
 } // namespace wrapper
 } // namespace ck

include/ck/wrapper/utils/tensor_utils.hpp

@@ -5,6 +5,7 @@
 
 #include "ck/ck.hpp"
 
+#include "ck/utility/data_type.hpp"
 #include "ck/utility/number.hpp"
 #include "ck/utility/tuple.hpp"
 #include "ck/utility/tuple_helper.hpp"
@@ -19,9 +20,9 @@ namespace wrapper {
  * \brief Memory type, allowed members:
  * - Generic,
  * - Global,
- * - LDS,
- * - SGPR,
- * - VGPR,
+ * - Lds,
+ * - Sgpr,
+ * - Vgpr,
  */
 using MemoryTypeEnum = AddressSpaceEnum;
 
@@ -52,12 +53,8 @@ struct Slice
     __host__ __device__ constexpr auto range(const T& dim) const
     {
         if constexpr(is_same_v<FromType, index_t> || is_same_v<ToType, index_t> ||
-                     is_same_v<T, index_t>)
+                     is_same_v<std::remove_const_t<T>, index_t>)
         {
-            if(!(dim >= to_ && from_ >= 0 && (to_ < 0 || to_ > from_)))
-            {
-                throw std::runtime_error("Invalid range");
-            }
             if(to_ < 0)
             {
                 return dim - from_ + to_ + 1;
@@ -70,9 +67,10 @@ struct Slice
         }
         else
         {
-            static_assert(dim >= to_ && from_ >= Number<0>{} && (to_ < 0 || to_ > from_),
+            static_assert(T{} >= ToType{} && FromType{} >= Number<0>{} &&
+                              (ToType{} < 0 || ToType{} > FromType{}),
                           "Invalid range");
-            if constexpr(to_ < 0)
+            if constexpr(ToType{} < 0)
             {
                 return dim - from_ + to_ + Number<1>{};
             }
@@ -130,6 +128,23 @@ constexpr auto make_register_tensor(const Layout<Shape, UnrolledDescriptorType>&
     return Tensor<MemoryType, ElementType, Shape, UnrolledDescriptorType>(layout);
 }
 
+/**
+ * \brief Clear tensor. (Only for Vpgr/Sgpr)
+ *
+ * \param tensor Tensor to be cleared.
+ */
+template <MemoryTypeEnum BufferAddressSpace,
+          typename ElementType,
+          typename Shape,
+          typename UnrolledDescriptorType>
+__host__ __device__ void
+clear(Tensor<BufferAddressSpace, ElementType, Shape, UnrolledDescriptorType>& tensor)
+{
+    static_assert(
+        !Tensor<BufferAddressSpace, ElementType, Shape, UnrolledDescriptorType>::IsDynamicBuffer);
+    return tensor.GetBuffer().Clear();
+}
+
 /**
  * \brief Get Tensor Layout.
  *

script/parse_perf_data.py

-#!/usr/bin/env python3
-import os, io, argparse, datetime, re
-import numpy as np
-import sqlalchemy
-from sqlalchemy.types import NVARCHAR, Float, Integer
-import pymysql
-import pandas as pd
-from sshtunnel import SSHTunnelForwarder
-
-def print_to_string(*args, **kwargs):
-    output = io.StringIO()
-    print(*args, file=output, **kwargs)
-    contents = output.getvalue()
-    output.close()
-    return contents
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='Parse results from tf benchmark runs')
-    parser.add_argument('filename', type=str, help='Log file to prase or directory containing log files')
-    args = parser.parse_args()
-    files = []
-    if os.path.isdir(args.filename):
-        all_files = os.listdir(args.filename)
-        for name in all_files:
-            if not 'log' in name:
-                continue
-            files.append(os.path.join(args.filename, name))
-    else:
-        files = [args.filename]
-    args.files = files
-    return args
-
-def main():
-    args = parse_args()
-    tests = []
-    kernels=[]
-    tflops=[]
-    dtype=[]
-    alayout=[]
-    blayout=[]
-    M=[]
-    N=[]
-    K=[]
-    StrideA=[]
-    StrideB=[]
-    StrideC=[]
-    #parse results, get the Tflops value for "Best Perf" kernels
-
-    glue=""
-    for filename in args.files:
-        for line in open(filename):
-            if 'Branch name' in line:
-                lst=line.split()
-                branch_name=lst[2]
-            if 'On branch' in line:
-                lst=line.split()
-                branch_name=lst[2]
-            if 'Node name' in line:
-                lst=line.split()
-                node_id=lst[2]
-            if 'GPU_arch' in line:
-                lst=line.split()
-                gpu_arch=lst[2]
-            if 'HIP version' in line:
-                lst=line.split()
-                hip_vers=lst[2]
-            if 'Compute Unit' in line:
-                lst=line.split()
-                compute_units=lst[2]
-            if 'InstalledDir' in line:
-                lst=line.split()
-                rocm_vers=lst[1][lst[1].find('/opt/rocm-')+len('/opt/rocm-'):lst[1].rfind('/llvm/bin')]
-    print("Branch name:",branch_name)
-    print("Node name:",node_id)
-    print("GPU_arch:",gpu_arch)
-    print("Compute units:",compute_units)
-    print("ROCM_version:",rocm_vers)
-    print("HIP_version:",hip_vers)
-
-
-    #parse gemm performance tests:
-    if 'gemm' in filename:
-        for filename in args.files:
-            for line in open(filename):
-                if 'Best Perf' in line:
-                    lst=line.split()
-                    if len(lst)>=37: #the line is complete
-                        tests.append(glue.join(lst[5:30]))
-                        kernels.append(glue.join(lst[37:]))
-                        tflops.append(lst[33])
-                        dtype.append(lst[5])
-                        alayout.append(lst[8])
-                        blayout.append(lst[11])
-                        M.append(lst[14])
-                        N.append(lst[17])
-                        K.append(lst[20])
-                        StrideA.append(lst[23])
-                        StrideB.append(lst[26])
-                        StrideC.append(lst[29])
-                    elif len(lst)<37 and len(lst)>=33: #the tflops are available
-                        tests.append(glue.join(lst[5:30]))
-                        kernels.append("N/A")
-                        tflops.append(lst[33])
-                        dtype.append(lst[5])
-                        alayout.append(lst[8])
-                        blayout.append(lst[11])
-                        M.append(lst[14])
-                        N.append(lst[17])
-                        K.append(lst[20])
-                        StrideA.append(lst[23])
-                        StrideB.append(lst[26])
-                        StrideC.append(lst[29])
-                        print("warning: incomplete line:",lst)
-                    elif len(lst)<33: #even the tflops are not available
-                        print("Error in ckProfiler output!")
-                        print("warning: incomplete line=",lst)
-        #sort results
-        #sorted_tests = sorted(tests)
-        #print("sorted tests:",sorted_tests)
-        sorted_tflops = [x for _,x in sorted(zip(tests,tflops))]
-        #sorted_kernels = [x for _,x in sorted(zip(tests,kernels))]
-        test_list=list(range(1,len(tests)+1))
-
-    #parse resnet50 performance tests:
-    if 'resnet50' in filename:
-        for filename in args.files:
-            for line in open(filename):
-                if 'Best Perf' in line:
-                    lst=line.split()
-                    tflops.append(lst[4])
-
-    print("Number of tests:",len(tflops))
-    sql_hostname = '127.0.0.1'
-    sql_username = os.environ["dbuser"]
-    sql_password = os.environ["dbpassword"]
-    sql_main_database = 'miopen_perf'
-    sql_port = 3306
-    ssh_host = os.environ["dbsship"]
-    ssh_user = os.environ["dbsshuser"]
-    ssh_port = int(os.environ["dbsshport"])
-    ssh_pass = os.environ["dbsshpassword"]
-
-    with SSHTunnelForwarder(
-            (ssh_host, ssh_port),
-            ssh_username=ssh_user,
-            ssh_password=ssh_pass,
-            remote_bind_address=(sql_hostname, sql_port)) as tunnel:
-
-        sqlEngine = sqlalchemy.create_engine('mysql+pymysql://{0}:{1}@{2}:{3}/{4}'.
-            format(sql_username, sql_password, sql_hostname, tunnel.local_bind_port, sql_main_database))
-        conn = sqlEngine.connect()
-
-        #save gemm performance tests:
-        if 'gemm' in filename:
-
-            #write the ck_gemm_test_params table
-            #only needed once the test set changes
-            '''
-            sorted_dtypes = [x for _,x in sorted(zip(tests,dtype))]
-            sorted_alayout = [x for _,x in sorted(zip(tests,alayout))]
-            sorted_blayout = [x for _,x in sorted(zip(tests,blayout))]
-            sorted_M = [x for _,x in sorted(zip(tests,M))]
-            sorted_N = [x for _,x in sorted(zip(tests,N))]
-            sorted_K = [x for _,x in sorted(zip(tests,K))]
-            sorted_StrideA = [x for _,x in sorted(zip(tests,StrideA))]
-            sorted_StrideB = [x for _,x in sorted(zip(tests,StrideB))]
-            sorted_StrideC = [x for _,x in sorted(zip(tests,StrideC))]
-            ck_gemm_params=[test_list,sorted_dtypes,sorted_alayout,sorted_blayout,
-                        sorted_M,sorted_N,sorted_K,sorted_StrideA,sorted_StrideB,
-                        sorted_StrideC]
-            df=pd.DataFrame(np.transpose(ck_gemm_params),columns=['Test_number','Data_type',
-                'Alayout','BLayout','M','N','K', 'StrideA','StrideB','StrideC'])
-            print(df)
-
-            dtypes = {
-                'Test_number': Integer(),
-                'Data_type': NVARCHAR(length=5),
-                'Alayout': NVARCHAR(length=12),
-                'Blayout': NVARCHAR(length=12),
-                'M': Integer(),
-                'N': Integer(),
-                'K': Integer(),
-                'StrideA': Integer(),
-                'StrideB': Integer(),
-                'StrideC': Integer()
-                }
-            df.to_sql("ck_gemm_test_params",conn,if_exists='replace',index=False, dtype=dtypes)
-            '''
-
-            #read baseline results for the latest develop branch
-            query = '''SELECT * from ck_gemm_tflops WHERE Datetime = (SELECT MAX(Datetime) FROM ck_gemm_tflops where Branch_ID='develop' );'''
-            tflops_base = pd.read_sql_query(query, conn)
-
-            #write new results to the db
-            testlist=[]
-            for i in range(1,len(tests)+1):
-                testlist.append("Test%i"%i)
-            ck_gemm_tflops=[str(branch_name),str(node_id),str(gpu_arch),compute_units,str(rocm_vers),str(hip_vers),str(datetime.datetime.now())]
-            flops=pd.DataFrame(data=[ck_gemm_tflops],columns=['Branch_ID','Node_ID','GPU_arch','Compute Units','ROCM_version','HIP_version','Datetime'])
-            df_add=pd.DataFrame(data=[sorted_tflops],columns=testlist)
-            flops=pd.concat([flops,df_add],axis=1)
-            print("new tflops for gemm tests:",flops)
-            flops.to_sql("ck_gemm_tflops",conn,if_exists='append',index=False)
-
-        #save resnet50 performance tests:
-        if 'resnet50' in filename:
-            #read baseline results for the latest develop branch
-            query = '''SELECT * from ck_resnet50_N256_tflops WHERE Datetime = (SELECT MAX(Datetime) FROM ck_resnet50_N256_tflops where Branch_ID='develop' );'''
-            tflops_base_N256 = pd.read_sql_query(query, conn)
-            query = '''SELECT * from ck_resnet50_N4_tflops WHERE Datetime = (SELECT MAX(Datetime) FROM ck_resnet50_N4_tflops where Branch_ID='develop' );'''
-            tflops_base_N4 = pd.read_sql_query(query, conn)
-
-            #write new results to the db
-            testlist=[]
-            for i in range(1,50):
-                testlist.append("Layer%i"%i)
-            ck_resnet_tflops=[str(branch_name),str(node_id),str(gpu_arch),compute_units,str(rocm_vers),str(hip_vers),str(datetime.datetime.now())]
-            flops0=pd.DataFrame(data=[ck_resnet_tflops],columns=['Branch_ID','Node_ID','GPU_arch','Compute Units','ROCM_version','HIP_version','Datetime'])
-            df_add=pd.DataFrame(data=[tflops[0:49]],columns=testlist)
-            flops=pd.concat([flops0,df_add],axis=1)
-            print("new tflops for N=256 resnet50 test:",flops)
-            flops.to_sql("ck_resnet50_N256_tflops",conn,if_exists='append',index=False)
-            df_add=pd.DataFrame(data=[tflops[49:98]],columns=testlist)
-            flops=pd.concat([flops0,df_add],axis=1)
-            print("new tflops for N=4 resnet50 test:",flops)
-            flops.to_sql("ck_resnet50_N4_tflops",conn,if_exists='append',index=False)
-
-        conn.close()
-
-    #compare the results to the baseline if baseline exists
-    regression=0
-    if 'gemm' in filename:
-        if not tflops_base.empty:
-            base=tflops_base[testlist].to_numpy(dtype='float')
-            base_list=base[0]
-            ave_perf=0
-            for i in range(len(base_list)):
-                # success criterion:
-                if base_list[i]>1.01*float(sorted_tflops[i]):
-                    print("test # ",i,"shows regression by {:.3f}%".format(
-                        (float(sorted_tflops[i])-base_list[i])/base_list[i]*100))
-                    regression=1
-                ave_perf=ave_perf+float(sorted_tflops[i])/base_list[i]
-            if regression==0:
-                print("no regressions found")
-            ave_perf=ave_perf/len(base_list)
-            print("average performance relative to baseline:",ave_perf)
-        else:
-            print("could not find a baseline")
-    if 'resnet50' in filename:
-        if not tflops_base_N256.empty:
-            base=tflops_base_N256[testlist].to_numpy(dtype='float')
-            base_list=base[0]
-            ave_perf=0
-            for i in range(len(base_list)):
-                # success criterion:
-                if base_list[i]>1.01*float(tflops[i]):
-                    print("layer # ",i,"shows regression by {:.3f}%".format(
-                        (float(tflops[i])-base_list[i])/base_list[i]*100))
-                    regression=1
-                ave_perf=ave_perf+float(tflops[i])/base_list[i]
-            if regression==0:
-                print("no regressions found")
-            ave_perf=ave_perf/len(base_list)
-            print("average performance relative to baseline:",ave_perf)
-        else:
-            print("could not find a baseline for N=256")
-        if not tflops_base_N4.empty:
-            base=tflops_base_N4[testlist].to_numpy(dtype='float')
-            base_list=base[0]
-            ave_perf=0
-            for i in range(len(base_list)):
-                # success criterion:
-                if base_list[i]>1.01*float(tflops[i+49]):
-                    print("layer # ",i,"shows regression by {:.3f}%".format(
-                        (float(tflops[i+49])-base_list[i])/base_list[i]*100))
-                    regression=1
-                ave_perf=ave_perf+float(tflops[i+49])/base_list[i]
-            if regression==0:
-                print("no regressions found")
-            ave_perf=ave_perf/len(base_list)
-            print("average performance relative to baseline:",ave_perf)
-        else:
-            print("could not find a baseline for N=4")
-
-    #return 0 if performance criteria met, otherwise return 1
-    return regression
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file

script/process_perf_data.py

@@ -133,7 +133,7 @@ def parse_logfile(logfile):
             if 'Best Perf' in line:
                 lst=line.split()
                 res.append(lst[4])
-    elif 'onnx_gemm' in logfile or 'splitK_gemm' in logfile:
+    elif 'onnx_gemm' in logfile or 'splitK_gemm' in logfile or 'mixed_gemm' in logfile:
         for line in open(logfile):
             if 'Best Perf' in line:
                 lst=line.split()
@@ -295,6 +295,10 @@ def main():
             for i in range(1,len(results)+1):
                 testlist.append("Test%i"%i)
             table_name="ck_splitK_gemm_tflops"
+        if 'mixed_gemm' in filename:
+            for i in range(1,len(results)+1):
+                testlist.append("Test%i"%i)
+            table_name="ck_mixed_gemm_tflops"
 
         tflops_base = get_baseline(table_name,conn)
         store_new_test_result(table_name, results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, conn)

script/profile_mixed_gemm.sh

+#!/bin/bash
+
+## GPU visibility
+export HIP_VISIBLE_DEVICES=0
+DRIVER="../build/bin/ckProfiler"
+echo $DRIVER
+OP=$1
+DATATYPE=$2
+LAYOUT=$3
+VERIFY=$4
+INIT=$5
+LOG=$6
+TIME=$7
+KBatch=$8
+
+########  op  datatype  layout  verify  init  log  time  M___ N___ K___  StrideA StrideB StrideC  KBatch_
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME    16    16 1024       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME    16    16 8192       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME    16    16 65536      -1     -1      -1   $KBatch
+
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME    16  2048 1024       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME    16  2048 8192       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME    16  2048 65536      -1     -1      -1   $KBatch
+
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME    16  8192 1024       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME    16  8192 8192       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME    16  8192 65536      -1     -1      -1   $KBatch
+
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  2048    16 1024       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  2048    16 8192       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  2048    16 65536      -1     -1      -1   $KBatch
+
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  2048  2048 1024       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  2048  2048 8192       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  2048  2048 65536      -1     -1      -1   $KBatch
+
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  2048  8192 1024       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  2048  8192 8192       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  2048  8192 65536      -1     -1      -1   $KBatch
+
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  8192    16 1024       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  8192    16 8192       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  8192    16 65536      -1     -1      -1   $KBatch
+
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  8192  2048 1024       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  8192  2048 8192       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  8192  2048 65536      -1     -1      -1   $KBatch
+
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  8192  8192 1024       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  8192  8192 8192       -1     -1      -1   $KBatch
+ $DRIVER $OP $DATATYPE $LAYOUT $VERIFY $INIT $LOG $TIME  8192  8192 65536      -1     -1      -1   $KBatch
+ 
\ No newline at end of file

script/run_full_performance_tests.sh

@@ -147,3 +147,9 @@ export onnx_log="perf_onnx_gemm.log"
 print_log_header $onnx_log $env_type $branch $host_name
 ./profile_onnx_gemm.sh gemm 0 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
 ./profile_onnx_gemm.sh gemm 1 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
+
+#run mixed fp16/fp8 and fp8/fp16 gemm tests
+export mixed_gemm_log="perf_mixed_gemm.log"
+print_log_header $mixed_gemm_log $env_type $branch $host_name
+./profile_mixed_gemm.sh gemm_splitk 4 0 $verify 2 0 1 16 2>&1 | tee -a $mixed_gemm_log
+./profile_mixed_gemm.sh gemm_splitk 5 0 $verify 2 0 1 16 2>&1 | tee -a $mixed_gemm_log
\ No newline at end of file

test/wrapper/CMakeLists.txt

@@ -6,3 +6,9 @@ add_gtest_executable(test_copy test_copy.cpp)
 target_link_libraries(test_copy PRIVATE utility)
 add_gtest_executable(test_partition test_partition.cpp)
 target_link_libraries(test_partition PRIVATE utility)
+if(GPU_TARGETS MATCHES "gfx908" OR GPU_TARGETS MATCHES "gfx90a" OR
+   GPU_TARGETS MATCHES "gfx940" OR GPU_TARGETS MATCHES "gfx941" OR
+   GPU_TARGETS MATCHES "gfx942")
+    add_gtest_executable(test_gemm test_gemm.cpp)
+    target_link_libraries(test_gemm PRIVATE utility)
+endif()

test/wrapper/test_gemm.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <numeric>
+#include <cstdlib>
+#include <iostream>
+#include <initializer_list>
+#include <vector>
+#include <gtest/gtest.h>
+
+#include "ck/library/utility/host_tensor.hpp"
+
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+
+#include "ck/host_utility/kernel_launch.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/utility/common_header.hpp"
+#include "ck/library/utility/fill.hpp"
+#include "ck/wrapper/layout.hpp"
+#include "ck/wrapper/tensor.hpp"
+#include "ck/wrapper/operations/copy.hpp"
+#include "ck/wrapper/operations/gemm.hpp"
+
+template <typename DataType>
+void CheckResult(const std::vector<DataType>& a_data,
+                 const std::vector<DataType>& b_data,
+                 std::vector<DataType>& c_m_n_device_result,
+                 const ck::index_t M,
+                 const ck::index_t N,
+                 const ck::index_t K)
+{
+    using PassThrough           = ck::tensor_operation::element_wise::PassThrough;
+    using ReferenceGemmInstance = ck::tensor_operation::host::
+        ReferenceGemm<DataType, DataType, DataType, float, PassThrough, PassThrough, PassThrough>;
+
+    Tensor<DataType> a_m_k(HostTensorDescriptor({M, K}));
+    Tensor<DataType> b_k_n(HostTensorDescriptor({K, N}, {1, K}));
+    Tensor<DataType> c_m_n_host_result(HostTensorDescriptor({M, N}));
+
+    a_m_k.mData = a_data;
+    b_k_n.mData = b_data;
+
+    auto ref_op       = ReferenceGemmInstance{};
+    auto ref_invoker  = ref_op.MakeInvoker();
+    auto ref_argument = ref_op.MakeArgument(
+        a_m_k, b_k_n, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
+
+    ref_invoker.Run(ref_argument);
+    EXPECT_TRUE(ck::utils::check_err(c_m_n_device_result, c_m_n_host_result.mData));
+}
+
+template <typename DataType,
+          typename GemmTraits,
+          ck::index_t scalar_per_vector,
+          typename BlockShape,
+          typename ThreadLayoutShape>
+__global__ void DeviceGemm(const void* p_a,
+                           const void* p_b,
+                           void* p_c,
+                           const ck::index_t M,
+                           const ck::index_t N,
+                           const ck::index_t K,
+                           const BlockShape tile_shape,
+                           const ThreadLayoutShape thread_layout)
+{
+    constexpr auto MPerBlock = ck::wrapper::size<0>(tile_shape);
+    constexpr auto NPerBlock = ck::wrapper::size<1>(tile_shape);
+    constexpr auto KPerBlock = ck::wrapper::size<2>(tile_shape);
+
+    const auto a_global_layout =
+        ck::wrapper::make_layout(ck::make_tuple(M, K), ck::make_tuple(K, 1));
+    const auto b_global_layout =
+        ck::wrapper::make_layout(ck::make_tuple(N, K), ck::make_tuple(K, 1));
+    const auto c_global_layout =
+        ck::wrapper::make_layout(ck::make_tuple(M, N), ck::make_tuple(N, 1));
+
+    constexpr auto a_tile_layout = ck::wrapper::make_layout(
+        ck::make_tuple(MPerBlock, KPerBlock), ck::make_tuple(KPerBlock, ck::Number<1>{}));
+    constexpr auto b_tile_layout = ck::wrapper::make_layout(
+        ck::make_tuple(NPerBlock, KPerBlock), ck::make_tuple(KPerBlock, ck::Number<1>{}));
+    constexpr auto c_tile_layout = ck::wrapper::make_layout(
+        ck::make_tuple(MPerBlock, NPerBlock), ck::make_tuple(NPerBlock, ck::Number<1>{}));
+
+    auto a_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
+        static_cast<const DataType*>(p_a), a_global_layout);
+    auto b_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
+        static_cast<const DataType*>(p_b), b_global_layout);
+    auto c_global_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
+        static_cast<DataType*>(p_c), c_global_layout);
+
+    auto a_padded_global_tensor = ck::wrapper::pad(a_global_tensor, shape(a_tile_layout));
+    auto b_padded_global_tensor = ck::wrapper::pad(b_global_tensor, shape(b_tile_layout));
+    auto c_padded_global_tensor = ck::wrapper::pad(c_global_tensor, shape(c_tile_layout));
+
+    __shared__ DataType lds_a[ck::wrapper::size(a_tile_layout)];
+    __shared__ DataType lds_b[ck::wrapper::size(b_tile_layout)];
+
+    auto a_lds_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
+        static_cast<DataType*>(lds_a), a_tile_layout);
+    auto b_lds_tensor = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
+        static_cast<DataType*>(lds_b), b_tile_layout);
+
+    const ck::index_t block_idx      = static_cast<ck::index_t>(blockIdx.x);
+    using DimAccessOrder             = ck::Tuple<ck::Number<0>, ck::Number<1>>;
+    constexpr ck::index_t vector_dim = 1;
+
+    auto c_global_local_tile = ck::wrapper::make_local_tile(
+        c_padded_global_tensor,
+        tile_shape,
+        block_idx,
+        make_tuple(ck::Number<1>{}, ck::Number<1>{}, ck::wrapper::slice(KPerBlock)));
+    auto c_global_local_partition =
+        ck::wrapper::make_blockwise_gemm_xdl_c_local_partition<DataType,
+                                                               decltype(a_tile_layout),
+                                                               decltype(b_tile_layout),
+                                                               ck::wrapper::size(thread_layout),
+                                                               GemmTraits>(c_global_local_tile);
+    auto c_vgpr_reg = ck::wrapper::make_blockwise_gemm_xdl_c_vgpr<DataType,
+                                                                  decltype(a_tile_layout),
+                                                                  decltype(b_tile_layout),
+                                                                  ck::wrapper::size(thread_layout),
+                                                                  GemmTraits>();
+    ck::wrapper::clear(c_vgpr_reg);
+
+    const ck::index_t num_loop = ck::math::integer_divide_ceil(K, KPerBlock);
+    ck::index_t i              = 0;
+    do
+    {
+        const auto k_slice = ck::wrapper::slice(i * KPerBlock, (i + 1) * KPerBlock);
+        auto a_padded_global_tensor_k_slice = a_padded_global_tensor(ck::wrapper::slice(), k_slice);
+        auto b_padded_global_tensor_k_slice = b_padded_global_tensor(ck::wrapper::slice(), k_slice);
+        auto a_global_local_tile            = ck::wrapper::make_local_tile(
+            a_padded_global_tensor_k_slice,
+            tile_shape,
+            block_idx,
+            make_tuple(ck::Number<1>{}, ck::wrapper::slice(N), ck::Number<1>{}));
+        auto b_global_local_tile = ck::wrapper::make_local_tile(
+            b_padded_global_tensor_k_slice,
+            tile_shape,
+            block_idx,
+            make_tuple(ck::wrapper::slice(M), ck::Number<1>{}, ck::Number<1>{}));
+
+        ck::wrapper::blockwise_copy<DimAccessOrder, vector_dim, scalar_per_vector>(
+            a_global_local_tile, a_lds_tensor, thread_layout);
+        ck::wrapper::blockwise_copy<DimAccessOrder, vector_dim, scalar_per_vector>(
+            b_global_local_tile, b_lds_tensor, thread_layout);
+        ck::block_sync_lds();
+        ck::wrapper::blockwise_gemm_xdl<DataType, ck::wrapper::size(thread_layout), GemmTraits>(
+            a_lds_tensor, b_lds_tensor, c_vgpr_reg);
+
+        ++i;
+    } while(i < num_loop);
+
+    ck::wrapper::copy(c_vgpr_reg, c_global_local_partition);
+}
+
+template <typename DataType,
+          typename GemmTraits,
+          ck::index_t scalar_per_vector,
+          typename BlockShape,
+          typename ThreadLayoutShape>
+void PerformGemm(const ck::index_t M,
+                 const ck::index_t N,
+                 const ck::index_t K,
+                 const BlockShape& tile_shape,
+                 const ThreadLayoutShape& thread_layout)
+{
+    // Global memory buffers
+    DeviceMem a_mem(M * K * sizeof(DataType));
+    DeviceMem b_mem(K * N * sizeof(DataType));
+    DeviceMem c_mem(M * N * sizeof(DataType));
+
+    std::vector<DataType> a_data(M * K);
+    std::vector<DataType> b_data(K * N);
+    ck::utils::FillUniformDistributionIntegerValue<DataType>{-5.f, 5.f}(a_data);
+    ck::utils::FillUniformDistributionIntegerValue<DataType>{-5.f, 5.f}(b_data);
+
+    a_mem.ToDevice(a_data.data());
+    b_mem.ToDevice(b_data.data());
+    c_mem.SetZero();
+
+    const ck::index_t grid_size =
+        ck::math::integer_divide_ceil(M, ck::wrapper::size<0>(tile_shape)) *
+        ck::math::integer_divide_ceil(N, ck::wrapper::size<1>(tile_shape));
+
+    const auto kernel =
+        DeviceGemm<DataType, GemmTraits, scalar_per_vector, BlockShape, ThreadLayoutShape>;
+    launch_and_time_kernel(StreamConfig{nullptr},
+                           kernel,
+                           dim3(grid_size),
+                           dim3(ck::wrapper::size(thread_layout)),
+                           0,
+                           a_mem.GetDeviceBuffer(),
+                           b_mem.GetDeviceBuffer(),
+                           c_mem.GetDeviceBuffer(),
+                           M,
+                           N,
+                           K,
+                           tile_shape,
+                           thread_layout);
+
+    std::vector<DataType> c_data(M * N);
+    c_mem.FromDevice(c_data.data());
+
+    CheckResult<DataType>(a_data, b_data, c_data, M, N, K);
+}
+
+TEST(TestGemm, Float)
+{
+    using DataType           = float;
+    const auto thread_layout = ck::make_tuple(ck::Number<16>{}, ck::Number<16>{});
+    const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 4>(
+        512, 512, 128, tile_shape, thread_layout);
+    // Irregular case
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 1>(
+        129, 129, 67, tile_shape, thread_layout);
+}
+
+TEST(TestGemm, Int8)
+{
+    using DataType           = int8_t;
+    const auto thread_layout = ck::make_tuple(ck::Number<64>{}, ck::Number<4>{});
+    const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 16>(
+        512, 512, 128, tile_shape, thread_layout);
+    // Irregular case
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 1>(
+        129, 129, 67, tile_shape, thread_layout);
+}
+
+TEST(TestGemm, Half)
+{
+    using DataType           = ck::half_t;
+    const auto thread_layout = ck::make_tuple(ck::Number<32>{}, ck::Number<8>{});
+    const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 8>(
+        512, 512, 128, tile_shape, thread_layout);
+    // Irregular case
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x2XdlPerWave_4K1, 1>(
+        129, 129, 67, tile_shape, thread_layout);
+}
+
+TEST(TestGemm, Float_2x4_4x2_XdlPerWave)
+{
+    using DataType                            = float;
+    const auto thread_layout_4x2_xdl_per_wave = ck::make_tuple(ck::Number<16>{}, ck::Number<8>{});
+    const auto thread_layout_2x4_xdl_per_wave = ck::make_tuple(ck::Number<8>{}, ck::Number<16>{});
+    const auto tile_shape = ck::make_tuple(ck::Number<128>{}, ck::Number<128>{}, ck::Number<64>{});
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_4x2XdlPerWave_4K1, 4>(
+        512, 512, 128, tile_shape, thread_layout_4x2_xdl_per_wave);
+    PerformGemm<DataType, ck::wrapper::BlockwisGemmXdlTraits_32x32Xdl_2x4XdlPerWave_4K1, 4>(
+        512, 512, 128, tile_shape, thread_layout_2x4_xdl_per_wave);
+}

test/wrapper/test_partition.cpp

@@ -29,17 +29,24 @@ TEST(TestPartition, LocalPartition)
     const auto tensor =
         ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Generic>(data.data(), layout);
 
-    const auto thread_steps  = ck::make_tuple(ck::Number<8>{}, ck::Number<1>{});
-    const auto thread_layout = ck::make_tuple(ck::Number<8>{}, ck::Number<1>{});
+    const auto thread_steps  = ck::make_tuple(ck::Number<1>{}, ck::Number<8>{}, ck::Number<1>{});
+    const auto thread_layout = ck::make_tuple(ck::Number<4>{}, ck::Number<8>{}, ck::Number<1>{});
+    // 3d partition on 2d shape (calculate partition on 3d thread layout, and then skip first dim)
+    const auto thread_projection =
+        ck::make_tuple(ck::wrapper::slice(4), ck::Number<1>{}, ck::Number<1>{});
+    constexpr ck::index_t projection_thread_length = ck::Number<4>{};
 
-    for(ck::index_t thread_id = 0; thread_id < ck::wrapper::size(thread_layout); thread_id++)
+    for(ck::index_t thread_id = 0;
+        thread_id < ck::wrapper::size(thread_layout) / projection_thread_length;
+        thread_id++)
     {
         const auto packed_partition =
-            ck::wrapper::make_local_partition(tensor, thread_layout, thread_id);
+            ck::wrapper::make_local_partition(tensor, thread_layout, thread_id, thread_projection);
 
         const auto expected_partition_size =
-            ck::wrapper::size(tensor) / ck::wrapper::size(thread_layout);
-        const auto expected_partition_first_val  = thread_id * ck::wrapper::size<0>(thread_steps);
+            ck::wrapper::size(tensor) /
+            (ck::wrapper::size(thread_layout) / projection_thread_length);
+        const auto expected_partition_first_val  = thread_id * ck::wrapper::size<1>(thread_steps);
         const auto expected_partition_second_val = expected_partition_first_val + 1;
         EXPECT_EQ(ck::wrapper::size(packed_partition), expected_partition_size);
         EXPECT_EQ(packed_partition(0), expected_partition_first_val);
@@ -58,8 +65,12 @@ TEST(TestPartition, LocalTile)
 
     const auto tensor =
         ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Generic>(data.data(), layout);
-
-    const auto block_shape = ck::make_tuple(ck::Number<2>{}, ck::Number<4>{}, ck::Number<2>{});
+    // 4d tile partitioning on 3d shape (calculate tile on 4d tile layout, and then skip last dim)
+    const auto block_shape =
+        ck::make_tuple(ck::Number<2>{}, ck::Number<4>{}, ck::Number<2>{}, ck::Number<2>{});
+    const auto block_projection =
+        ck::make_tuple(ck::Number<1>{}, ck::Number<1>{}, ck::Number<1>{}, ck::wrapper::slice(2));
+    constexpr ck::index_t projection_block_dim = ck::Number<2>{};
     const auto num_blocks =
         ck::make_tuple(ck::wrapper::size<0>(shape) / ck::wrapper::size<0>(block_shape),
                        ck::wrapper::size<1>(shape) / ck::wrapper::size<1>(block_shape),
@@ -69,9 +80,10 @@ TEST(TestPartition, LocalTile)
 
     for(auto block_idx : block_idxs)
     {
-        const auto packed_tile = ck::wrapper::make_local_tile(tensor, block_shape, block_idx);
+        const auto packed_tile =
+            ck::wrapper::make_local_tile(tensor, block_shape, block_idx, block_projection);
 
-        const auto expected_tile_size = ck::wrapper::size(block_shape);
+        const auto expected_tile_size = ck::wrapper::size(block_shape) / projection_block_dim;
         auto expected_tile_first_val  = (block_idx % ck::wrapper::size<2>(num_blocks)) *
                                        ck::wrapper::size<2>(block_shape) *
                                        ck::wrapper::size<2>(strides);