Merge remote-tracking branch 'origin/develop' into aosewski/ggemm_multi_d2

test/normalization_bwd_gamma_beta/test_groupnorm_bwd_gamma_beta_fp32.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "gtest/gtest.h"
+#include "profiler/profile_groupnorm_bwd_gamma_beta_impl.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+using ck::index_t;
+
+template <typename Tuple>
+class TestgroupnormBwdGammaBeta : public ::testing::Test
+{
+    protected:
+    using DYDataType         = std::tuple_element_t<0, Tuple>;
+    using XDataType          = std::tuple_element_t<1, Tuple>;
+    using MeanInvStdDataType = std::tuple_element_t<2, Tuple>;
+    using ComputeDataType    = std::tuple_element_t<3, Tuple>;
+    using DGammaDataType     = std::tuple_element_t<4, Tuple>;
+    using DBetaDataType      = std::tuple_element_t<5, Tuple>;
+
+    void Run()
+    {
+        // Bwd data: [N, H, W, G, C], reduce H, W, C
+        std::vector<std::vector<ck::index_t>> lengths = {{1, 1, 1, 1, 1},
+                                                         {1, 2, 3, 4, 5},
+                                                         {256, 9, 9, 9, 9},
+                                                         {1, 64, 64, 32, 10},
+                                                         {1, 32, 32, 32, 20},
+                                                         {1, 16, 16, 32, 40}};
+
+        for(auto length : lengths)
+        {
+            bool success = ck::profiler::profile_groupnorm_bwd_gamma_beta_impl<DYDataType,
+                                                                               XDataType,
+                                                                               MeanInvStdDataType,
+                                                                               ComputeDataType,
+                                                                               DGammaDataType,
+                                                                               DBetaDataType>(
+                true, 2, false, false, length);
+            EXPECT_TRUE(success);
+        }
+    }
+};
+
+using KernelTypes = ::testing::Types<
+    // DYDataType XDataType, MeanInvStdDataType, ComputeDataType, DGammaDataType, DBetaDataType>
+    std::tuple<F32, F32, F32, F32, F32, F32>>;
+
+TYPED_TEST_SUITE(TestgroupnormBwdGammaBeta, KernelTypes);
+TYPED_TEST(TestgroupnormBwdGammaBeta, Test_FP32) { this->Run(); }

test/normalization_bwd_gamma_beta/test_layernorm2d_bwd_gamma_beta_fp32.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "gtest/gtest.h"
+#include "profiler/profile_layernorm_bwd_gamma_beta_impl.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+using ck::index_t;
+
+template <typename Tuple>
+class TestLayernorm2dBwdGammaBeta : public ::testing::Test
+{
+    protected:
+    using DYDataType         = std::tuple_element_t<0, Tuple>;
+    using XDataType          = std::tuple_element_t<1, Tuple>;
+    using MeanInvStdDataType = std::tuple_element_t<2, Tuple>;
+    using ComputeDataType    = std::tuple_element_t<3, Tuple>;
+    using DGammaDataType     = std::tuple_element_t<4, Tuple>;
+    using DBetaDataType      = std::tuple_element_t<5, Tuple>;
+
+    void Run()
+    {
+        // Bwd data: [N, D], reduce D
+        std::vector<std::vector<ck::index_t>> lengths = {
+            {4, 256}, {8, 511}, {9, 1032}, {4, 2048}, {1, 8192}, {4000, 2000}};
+
+        for(auto length : lengths)
+        {
+            bool success = ck::profiler::profile_layernorm_bwd_gamma_beta_impl<DYDataType,
+                                                                               XDataType,
+                                                                               MeanInvStdDataType,
+                                                                               ComputeDataType,
+                                                                               DGammaDataType,
+                                                                               DBetaDataType,
+                                                                               2>(
+                true, 2, false, false, length);
+            EXPECT_TRUE(success);
+        }
+    }
+};
+
+using KernelTypes = ::testing::Types<
+    // DYDataType XDataType, MeanInvStdDataType, ComputeDataType, DGammaDataType, DBetaDataType>
+    std::tuple<F32, F32, F32, F32, F32, F32>>;
+
+TYPED_TEST_SUITE(TestLayernorm2dBwdGammaBeta, KernelTypes);
+TYPED_TEST(TestLayernorm2dBwdGammaBeta, Test_FP32) { this->Run(); }

test/transpose/test_transpose.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
-
-#include <tuple>
-
 #include "gtest/gtest.h"
-#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
-#include "test_transpose_util.hpp"
+#include "profiler/profile_transpose_impl.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;
+using ck::index_t;
 
 template <typename Tuple>
 class TestTranspose : public ::testing::Test
 {
+    protected:
+    using ADataType = std::tuple_element_t<0, Tuple>;
+    using BDataType = std::tuple_element_t<1, Tuple>;
+
+    void Run()
+    {
+        std::vector<std::vector<ck::index_t>> lengths = {
+            {4, 16, 16, 32, 5}, {8, 16, 16, 32, 8} /**{32, 16, 16, 32, 8},**/};
+
+        for(auto length : lengths)
+        {
+            bool success = ck::profiler::profile_transpose_impl<ADataType, BDataType, 5>(
+                true, 2, false, false, length);
+            EXPECT_TRUE(success);
+        }
+    }
 };
 
-// clang-format off
-using KernelTypes = ::testing::Types<
-    std::tuple<      F16,       F16>,
-    std::tuple<      F32,       F32>
-    >;
-// clang-format on
+using KernelTypes = ::testing::Types<std::tuple<F16, F16>, std::tuple<F32, F32>>;
 
 TYPED_TEST_SUITE(TestTranspose, KernelTypes);
-
-//#include "test_transpose_ut_cases.inc"
+TYPED_TEST(TestTranspose, Test_FP16) { this->Run(); }
+TYPED_TEST(TestTranspose, Test_FP32) { this->Run(); }

test/transpose/test_transpose_ut_cases.inc

-#pragma once
-
-TYPED_TEST(TestTranspose, Test1)
-{
-    // for 16, 8, 16, 32, 8
-    std::vector<int> Ms{1, 2, 3, 4, 5, 6};
-    std::vector<index_t> lengths{16, 8, 16, 32, 8};
-    /**constexpr int N = 16;
-    constexpr int C = 8;
-    constexpr int D = 16;
-    constexpr int H = 32;
-    constexpr int W = 8;**/
-
-    this->Run();
-}
-
-TYPED_TEST(TestTranpose, Test2)
-{
-    std::vector<int> Ms{127, 255, 312, 799, 1573};
-    std::vector<index_t> lengths{16, 8, 16, 32, 16};
-    /**constexpr int N = 16;
-    constexpr int C = 8;
-    constexpr int D = 16;
-    constexpr int H = 32;
-    constexpr int W = 8;**/
-
-    this->Run();
-}

test/transpose/test_transpose_util.hpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
-
-#pragma once
-
-#include <string>
-#include <sstream>
-#include <tuple>
-#include <vector>
-#include <gtest/gtest.h>
-
-#include "ck/ck.hpp"
-#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
-#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
-#include "include/ck/utility/data_type.hpp"
-#include "profiler/profile_transpose_impl.hpp"
-
-namespace ck {
-namespace test {
-
-template <typename Tuple>
-class TestTranspose : public testing::Test
-{
-    using F32 = float;
-
-    protected:
-    using ADataType = std::tuple_element_t<0, Tuple>;
-    using BDataType = std::tuple_element_t<1, Tuple>;
-
-    public:
-    static constexpr bool verify_              = true;
-    static constexpr int init_method_          = 1; // decimal value initialization
-    static constexpr bool log_                 = false;
-    static constexpr bool bench_               = false; // measure kernel performance
-    std::vector<std::vector<index_t>> lengths_ = {{16, 32, 16, 32, 16}, {16, 8, 16, 32, 8}};
-
-    void Run()
-    {
-        for(auto length : this->lengths_)
-        {
-            this->RunSingle(length);
-        }
-    }
-
-    void RunSingle()
-    {
-        bool pass = ck::profiler::profile_transpose_impl<ADataType, BDataType, 5>(
-            verify_, init_method_, log_, bench_, lengths_);
-        EXPECT_TRUE(pass);
-    }
-};
-
-} // namespace test
-} // namespace ck

test/wrapper/CMakeLists.txt

@@ -2,3 +2,7 @@ add_gtest_executable(test_layout test_layout.cpp)
 target_link_libraries(test_layout PRIVATE utility)
 add_gtest_executable(test_tensor test_tensor.cpp)
 target_link_libraries(test_tensor PRIVATE utility)
+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)

test/wrapper/test_copy.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <numeric>
+#include <cstdlib>
+#include <iostream>
+#include <initializer_list>
+#include <vector>
+#include <gtest/gtest.h>
+
+#include "ck/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/wrapper/layout.hpp"
+#include "ck/wrapper/tensor.hpp"
+#include "ck/wrapper/operations/copy.hpp"
+
+// Test copy from Global to Global through LDS and VGPR
+template <typename InputTensor,
+          typename OutputTensor,
+          typename BlockShape,
+          typename ThreadLayoutShape,
+          bool UseOptimizedCopy>
+__global__ void TestCopyDevice(const InputTensor input_tensor,
+                               OutputTensor output_tensor,
+                               const BlockShape tile_shape,
+                               const ThreadLayoutShape thread_layout)
+{
+    __shared__ ck::index_t p_shared[ck::wrapper::size(tile_shape)];
+    const auto tensor_lds = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(
+        p_shared, ck::wrapper::make_layout(tile_shape));
+
+    const auto block_idx = static_cast<ck::index_t>(blockIdx.x);
+
+    // Get local tiles for global memory
+    const auto input_local_tile = ck::wrapper::make_local_tile(input_tensor, tile_shape, block_idx);
+    const auto output_local_tile =
+        ck::wrapper::make_local_tile(output_tensor, tile_shape, block_idx);
+
+    // Get partition per thread
+    const auto input_local_partition =
+        ck::wrapper::make_local_partition(input_local_tile, thread_layout, threadIdx.x);
+    auto lds_local_partition =
+        ck::wrapper::make_local_partition(tensor_lds, thread_layout, threadIdx.x);
+    auto output_local_partition =
+        ck::wrapper::make_local_partition(output_local_tile, thread_layout, threadIdx.x);
+
+    // Allocate VGPR
+    auto tensor_vgpr =
+        ck::wrapper::make_register_tensor<ck::wrapper::MemoryTypeEnum::Vgpr, ck::index_t>(
+            layout(lds_local_partition));
+
+    // Perform copy
+    if constexpr(UseOptimizedCopy)
+    {
+        using DimAccessOrder                    = ck::Tuple<ck::Number<1>, ck::Number<0>>;
+        constexpr ck::index_t vector_dim        = 0;
+        constexpr ck::index_t scalar_per_vector = 2;
+        ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(input_local_partition,
+                                                                         lds_local_partition);
+        // TODO: Enable optimized copy for static buffers
+        ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(lds_local_partition,
+                                                                         tensor_vgpr);
+        ck::wrapper::copy<DimAccessOrder, vector_dim, scalar_per_vector>(tensor_vgpr,
+                                                                         output_local_partition);
+    }
+    else
+    {
+        ck::wrapper::copy(input_local_partition, lds_local_partition);
+        ck::wrapper::copy(lds_local_partition, tensor_vgpr);
+        ck::wrapper::copy(tensor_vgpr, output_local_partition);
+    }
+}
+
+template <bool UseOptimizedCopy>
+void PerformCopyGlobalToGlobalViaLDS()
+{
+    const auto shape =
+        ck::make_tuple(ck::make_tuple(ck::Number<2>{}, ck::Number<2>{}), ck::Number<256>{});
+    const auto strides =
+        ck::make_tuple(ck::make_tuple(ck::Number<1>{}, ck::Number<2>{}), ck::Number<4>{});
+    const auto layout = ck::wrapper::make_layout(shape, strides);
+
+    // 0, 1, 2, ..., size(shape) - 1
+    std::vector<ck::index_t> input_data(ck::wrapper::size(shape));
+    std::iota(input_data.begin(), input_data.end(), 0);
+
+    // Global memory buffers
+    DeviceMem in_buf(ck::wrapper::size(layout) * sizeof(ck::index_t));
+    DeviceMem out_buf(ck::wrapper::size(layout) * sizeof(ck::index_t));
+
+    in_buf.ToDevice(input_data.data());
+    out_buf.SetZero();
+
+    // Create tensors for global memory
+    const auto input_tensor_global = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
+        static_cast<const ck::index_t*>(in_buf.GetDeviceBuffer()), layout);
+    auto output_tensor_global = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(
+        static_cast<ck::index_t*>(out_buf.GetDeviceBuffer()), layout);
+
+    const auto thread_layout = ck::make_tuple(ck::Number<1>{}, ck::Number<32>{});
+    const auto tile_shape    = ck::make_tuple(ck::Number<4>{}, ck::Number<64>{});
+
+    const ck::index_t grid_size = ck::math::integer_divide_ceil(
+        ck::wrapper::size(input_tensor_global), ck::wrapper::size(tile_shape));
+
+    const auto kernel = TestCopyDevice<decltype(input_tensor_global),
+                                       decltype(output_tensor_global),
+                                       decltype(tile_shape),
+                                       decltype(thread_layout),
+                                       UseOptimizedCopy>;
+    launch_and_time_kernel(StreamConfig{},
+                           kernel,
+                           dim3(grid_size),
+                           dim3(ck::wrapper::size(thread_layout)),
+                           0,
+                           input_tensor_global,
+                           output_tensor_global,
+                           tile_shape,
+                           thread_layout);
+
+    // Verify results
+    std::vector<ck::index_t> output_data(ck::wrapper::size(shape));
+    out_buf.FromDevice(output_data.data());
+    EXPECT_TRUE(ck::utils::check_err(output_data, input_data));
+}
+
+TEST(TestCopyGlobalToGlobalViaLDS, GenericCopy) { PerformCopyGlobalToGlobalViaLDS<false>(); }
+TEST(TestCopyGlobalToGlobalViaLDS, OptimizedCopy) { PerformCopyGlobalToGlobalViaLDS<true>(); }

test/wrapper/test_layout.cpp

@@ -84,7 +84,8 @@ TEST_F(TestWrapperLayout, 2d)
         ck::make_tuple(ck::Sequence<0>{}));
     const auto layout_runtime = ck::wrapper::make_layout(ck::make_tuple(d1, d0));
     const auto layout_compiletime =
-        ck::wrapper::make_layout(ck::make_tuple(ck::Number<d1>{}, ck::Number<d0>{}));
+        ck::wrapper::make_layout(ck::make_tuple(ck::Number<d1>{}, ck::Number<d0>{}),
+                                 ck::make_tuple(ck::Number<s1>{}, ck::Number<s0>{}));
     std::vector<ck::Tuple<ck::index_t, ck::index_t>> idxs;
 
     for(ck::index_t h = 0; h < d1; h++)
@@ -435,19 +436,11 @@ TEST(TestLayoutHelpers, ShapeAndStrides)
     constexpr bool check_compiletime_shape =
         std::is_same_v<decltype(shape_compiletime),
                        std::remove_reference_t<decltype(shape(layout_compiletime))>>;
-    constexpr bool check_compiletime_strides =
-        std::is_same_v<decltype(strides_compiletime),
-                       std::remove_reference_t<decltype(stride(layout_compiletime))>>;
     constexpr bool check_runtime_shape =
         std::is_same_v<decltype(shape_runtime),
                        std::remove_reference_t<decltype(shape(layout_runtime))>>;
-    constexpr bool check_runtime_strides =
-        std::is_same_v<decltype(strides_runtime),
-                       std::remove_reference_t<decltype(stride(layout_runtime))>>;
     EXPECT_TRUE(check_compiletime_shape);
-    EXPECT_TRUE(check_compiletime_strides);
     EXPECT_TRUE(check_runtime_shape);
-    EXPECT_TRUE(check_runtime_strides);
 }
 
 TEST(TestLayoutHelpers, Hierarchical)

test/wrapper/test_partition.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <numeric>
+#include <cstdlib>
+#include <iostream>
+#include <initializer_list>
+#include <vector>
+#include <gtest/gtest.h>
+
+#include "ck/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/wrapper/layout.hpp"
+#include "ck/wrapper/tensor.hpp"
+
+TEST(TestPartition, LocalPartition)
+{
+    const auto shape =
+        ck::make_tuple(ck::make_tuple(ck::Number<16>{}, ck::Number<4>{}), ck::Number<4>{});
+    const auto strides =
+        ck::make_tuple(ck::make_tuple(ck::Number<1>{}, ck::Number<16>{}), ck::Number<64>{});
+    const auto layout = ck::wrapper::make_layout(shape, strides);
+
+    std::vector<ck::index_t> data(ck::wrapper::size(layout));
+    std::iota(data.begin(), data.end(), 0);
+
+    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>{});
+
+    for(ck::index_t thread_id = 0; thread_id < ck::wrapper::size(thread_layout); thread_id++)
+    {
+        const auto packed_partition =
+            ck::wrapper::make_local_partition(tensor, thread_layout, thread_id);
+
+        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);
+        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);
+        EXPECT_EQ(packed_partition(1), expected_partition_second_val);
+    }
+}
+
+TEST(TestPartition, LocalTile)
+{
+    const auto shape   = ck::make_tuple(ck::Number<16>{}, ck::Number<4>{}, ck::Number<4>{});
+    const auto strides = ck::make_tuple(ck::Number<1>{}, ck::Number<16>{}, ck::Number<64>{});
+    const auto layout  = ck::wrapper::make_layout(shape, strides);
+
+    std::vector<ck::index_t> data(ck::wrapper::size(layout));
+    std::iota(data.begin(), data.end(), 0);
+
+    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>{});
+    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),
+                       ck::wrapper::size<2>(shape) / ck::wrapper::size<2>(block_shape));
+    std::vector<ck::index_t> block_idxs(ck::wrapper::size(num_blocks));
+    std::iota(block_idxs.begin(), block_idxs.end(), 0);
+
+    for(auto block_idx : block_idxs)
+    {
+        const auto packed_tile = ck::wrapper::make_local_tile(tensor, block_shape, block_idx);
+
+        const auto expected_tile_size = ck::wrapper::size(block_shape);
+        auto expected_tile_first_val  = (block_idx % ck::wrapper::size<2>(num_blocks)) *
+                                       ck::wrapper::size<2>(block_shape) *
+                                       ck::wrapper::size<2>(strides);
+        block_idx /= ck::wrapper::size<2>(num_blocks);
+        expected_tile_first_val += (block_idx % ck::wrapper::size<1>(num_blocks)) *
+                                   ck::wrapper::size<1>(block_shape) *
+                                   ck::wrapper::size<1>(strides);
+        block_idx /= ck::wrapper::size<1>(num_blocks);
+        expected_tile_first_val += (block_idx % ck::wrapper::size<0>(num_blocks)) *
+                                   ck::wrapper::size<0>(block_shape) *
+                                   ck::wrapper::size<0>(strides);
+
+        const auto expected_tile_second_val = expected_tile_first_val + 1;
+        EXPECT_EQ(ck::wrapper::size(packed_tile), expected_tile_size);
+        EXPECT_EQ(packed_tile(0), expected_tile_first_val);
+        EXPECT_EQ(packed_tile(1), expected_tile_second_val);
+    }
+}

test/wrapper/test_tensor.cpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #include <cstdlib>
 #include <iostream>
@@ -100,42 +100,34 @@ TEST(TestTensor, ReadWriteHostMemory)
 
 __global__ void TestTensorReadWriteDevice(void* data, void* success)
 {
-    constexpr ck::index_t nelems            = 8;
-    constexpr ck::index_t scalar_per_vector = 1;
+    constexpr ck::index_t nelems = 8;
     __shared__ ck::index_t p_shared[nelems];
 
     ck::index_t* casted_data_ptr = static_cast<ck::index_t*>(data);
     bool* casted_success_ptr     = static_cast<bool*>(success);
 
     const auto layout = ck::wrapper::make_layout(ck::make_tuple(ck::make_tuple(2, 2), 2));
-    constexpr auto register_layout = ck::wrapper::make_layout(ck::make_tuple(ck::Number<8>{}));
+    constexpr auto vgpr_layout =
+        ck::wrapper::make_layout(make_tuple(ck::Number<nelems>{}), make_tuple(ck::Number<1>{}));
 
     auto tensor_global =
         ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Global>(casted_data_ptr, layout);
-    auto tensor_lds  = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(p_shared, layout);
-    auto tensor_vgpr = ck::wrapper::make_register_tensor<ck::wrapper::MemoryTypeEnum::Vgpr,
-                                                         nelems,
-                                                         scalar_per_vector,
-                                                         ck::index_t>(register_layout);
-    auto tensor_sgpr = ck::wrapper::make_register_tensor<ck::wrapper::MemoryTypeEnum::Sgpr,
-                                                         nelems,
-                                                         scalar_per_vector,
-                                                         ck::index_t>(register_layout);
+    auto tensor_lds = ck::wrapper::make_tensor<ck::wrapper::MemoryTypeEnum::Lds>(p_shared, layout);
+    auto tensor_vgpr =
+        ck::wrapper::make_register_tensor<ck::wrapper::MemoryTypeEnum::Vgpr, ck::index_t>(
+            vgpr_layout);
 
     InitTensor(tensor_global);
     InitTensor(tensor_lds);
     StaticInitTensor<nelems>(tensor_vgpr);
-    StaticInitTensor<nelems>(tensor_sgpr);
 
-    *casted_success_ptr &= TestTensorCheck1d(tensor_global);
+    *casted_success_ptr = TestTensorCheck1d(tensor_global);
     *casted_success_ptr &= TestTensorCheck3d(tensor_global);
 
     *casted_success_ptr &= TestTensorCheck1d(tensor_lds);
     *casted_success_ptr &= TestTensorCheck3d(tensor_lds);
 
     *casted_success_ptr &= StaticTestTensorCheck1d<nelems>(tensor_vgpr);
-
-    *casted_success_ptr &= StaticTestTensorCheck1d<nelems>(tensor_sgpr);
 }
 
 TEST(TestTensor, ReadWriteGlobalLdsRegistersMemory)
@@ -151,7 +143,7 @@ TEST(TestTensor, ReadWriteGlobalLdsRegistersMemory)
                            TestTensorReadWriteDevice,
                            dim3(1),
                            dim3(1),
-                           nelems * sizeof(ck::index_t),
+                           0,
                            data_buf.GetDeviceBuffer(),
                            success_buf.GetDeviceBuffer());
 
@@ -173,33 +165,45 @@ TEST(TestTensor, Slicing)
 
     auto tensor2x2x2 =
         tensor(ck::make_tuple(ck::wrapper::slice(2), ck::wrapper::slice(2)), ck::wrapper::slice(2));
+    EXPECT_EQ(tensor2x2x2(0), layout(ck::make_tuple(ck::make_tuple(0, 0), 0)));
     EXPECT_EQ(ck::wrapper::rank(tensor2x2x2), 2);
     EXPECT_EQ(ck::wrapper::depth(tensor2x2x2), 2);
     EXPECT_EQ(ck::wrapper::size(tensor2x2x2), 8);
     EXPECT_TRUE(TestTensorCheck1d(tensor2x2x2));
 
     auto tensor2x2 = tensor(ck::make_tuple(1, ck::wrapper::slice(2)), ck::wrapper::slice(2));
+    EXPECT_EQ(tensor2x2(0), layout(ck::make_tuple(ck::make_tuple(1, 0), 0)));
     EXPECT_EQ(ck::wrapper::rank(tensor2x2), 2);
     EXPECT_EQ(ck::wrapper::depth(tensor2x2), 2);
     EXPECT_EQ(ck::wrapper::size(tensor2x2), 4);
-    EXPECT_TRUE(TestTensorCheck1d(tensor2x2, layout(ck::make_tuple(ck::make_tuple(1, 0), 0))));
+    EXPECT_TRUE(TestTensorCheck1d(tensor2x2));
 
     auto tensor1x1 = tensor(ck::make_tuple(1, ck::wrapper::slice(1, 2)), ck::wrapper::slice(1, 2));
+    EXPECT_EQ(tensor1x1(0), layout(ck::make_tuple(ck::make_tuple(1, 1), 1)));
     EXPECT_EQ(rank(tensor1x1), 2);
     EXPECT_EQ(depth(tensor1x1), 2);
     EXPECT_EQ(size(tensor1x1), 1);
-    EXPECT_TRUE(TestTensorCheck1d(tensor1x1, layout(ck::make_tuple(ck::make_tuple(1, 1), 1))));
+    EXPECT_TRUE(TestTensorCheck1d(tensor1x1));
 
     auto tensor2 = tensor(ck::make_tuple(1, 1), ck::wrapper::slice(0, 2));
+    EXPECT_EQ(tensor2(0), layout(ck::make_tuple(ck::make_tuple(1, 1), 0)));
     EXPECT_EQ(ck::wrapper::rank(tensor2), 1);
     EXPECT_EQ(ck::wrapper::depth(tensor2), 1);
     EXPECT_EQ(ck::wrapper::size(tensor2), 2);
-    EXPECT_TRUE(TestTensorCheck1d(tensor2, layout(ck::make_tuple(ck::make_tuple(1, 1), 0))));
+    EXPECT_TRUE(TestTensorCheck1d(tensor2));
+
+    auto tensor2_v2 = tensor(2, ck::wrapper::slice(0, 2));
+    EXPECT_EQ(tensor2_v2(0), layout(ck::make_tuple(2, 0)));
+    EXPECT_EQ(ck::wrapper::rank(tensor2_v2), 1);
+    EXPECT_EQ(ck::wrapper::depth(tensor2_v2), 1);
+    EXPECT_EQ(ck::wrapper::size(tensor2_v2), 2);
+    EXPECT_TRUE(TestTensorCheck1d(tensor2_v2));
 
     // negative indexing
     auto tensor1x2 = tensor(ck::make_tuple(1, ck::wrapper::slice(0, -2)), ck::wrapper::slice());
+    EXPECT_EQ(tensor1x2(0), layout(ck::make_tuple(ck::make_tuple(1, 0), 0)));
     EXPECT_EQ(rank(tensor1x2), 2);
     EXPECT_EQ(depth(tensor1x2), 2);
     EXPECT_EQ(size(tensor1x2), 2);
-    EXPECT_TRUE(TestTensorCheck1d(tensor1x2, layout(ck::make_tuple(ck::make_tuple(1, 0), 0))));
+    EXPECT_TRUE(TestTensorCheck1d(tensor1x2));
 }