Merge branch 'gridwise_2d' of github.com:ROCmSoftwarePlatform/composable_kernel into gridwise_2d

example/12_reduce/reduce_multiblock_atomic_add_impl.hpp

@@ -225,7 +225,7 @@ int reduce_multiblock_atomic_add_impl(bool do_verification,
     if(do_verification)
     {
         out_dev.FromDevice(out.mData.data());
-        pass = pass && ck::utils::check_err(out.mData, out_ref.mData);
+        pass = pass && ck::utils::check_err(out, out_ref);
     };
 
     return (pass ? 0 : 1);

example/13_pool2d_fwd/pool2d_fwd_common.hpp

@@ -16,6 +16,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 
 template <typename InDataType,
           typename OutDataType,
@@ -172,16 +173,16 @@ bool pool_test(bool do_verification,
     // tensor layout
     auto f_host_tensor_descriptor =
         [](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W, auto layout) {
+            using namespace ck::literals;
+
             if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value)
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
-                                            std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
+                return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, H * W, W, 1_uz});
             }
             else if constexpr(ck::is_same<decltype(layout),
                                           ck::tensor_layout::convolution::NHWC>::value)
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
-                                            std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
+                return HostTensorDescriptor({N_, C_, H, W}, {C_ * H * W, 1_uz, W * C_, C_});
             }
         };
 
@@ -267,14 +268,14 @@ bool pool_test(bool do_verification,
 
         out_device_buf.FromDevice(out_n_c_ho_wo_device.mData.data());
 
-        pass = pass && ck::utils::check_err(out_n_c_ho_wo_device.mData, out_n_c_ho_wo_host.mData);
+        pass = pass && ck::utils::check_err(out_n_c_ho_wo_device, out_n_c_ho_wo_host);
 
         if constexpr(OutputIndex)
         {
             out_indices_device_buf.FromDevice(out_indices_n_c_ho_wo_device.mData.data());
 
-            pass = pass && ck::utils::check_err(out_indices_n_c_ho_wo_device.mData,
-                                                out_indices_n_c_ho_wo_host.mData);
+            pass = pass &&
+                   ck::utils::check_err(out_indices_n_c_ho_wo_device, out_indices_n_c_ho_wo_host);
         };
     }
 

example/14_gemm_xdl_quantization/gemm_xdl_relu_quantization_int8.cpp

@@ -15,6 +15,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 #include "ck/library/utility/check_err.hpp"
 
@@ -133,15 +134,15 @@ int main(int argc, char* argv[])
 
     auto f_host_tensor_descriptor =
         [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
             if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                            std::vector<std::size_t>({stride, 1}));
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
             }
             else
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                            std::vector<std::size_t>({1, stride}));
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
             }
         };
 
@@ -225,7 +226,7 @@ int main(int argc, char* argv[])
 
         ref_invoker.Run(ref_argument);
 
-        return ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData) ? 0 : 1;
+        return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result) ? 0 : 1;
     }
 
     return 0;

example/15_grouped_gemm/grouped_gemm_xdl_bfp16.cpp

@@ -16,6 +16,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 
 template <ck::index_t... Is>

example/15_grouped_gemm/grouped_gemm_xdl_fp16.cpp

@@ -16,6 +16,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 
 template <ck::index_t... Is>

example/15_grouped_gemm/grouped_gemm_xdl_fp32.cpp

@@ -16,6 +16,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 
 template <ck::index_t... Is>

example/15_grouped_gemm/grouped_gemm_xdl_int4.cpp

@@ -16,6 +16,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 
 template <ck::index_t... Is>

example/15_grouped_gemm/grouped_gemm_xdl_int8.cpp

@@ -16,6 +16,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 
 template <ck::index_t... Is>

example/15_grouped_gemm/run_grouped_gemm_example.inc

@@ -52,15 +52,15 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
 
     auto f_host_tensor_descriptor =
         [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
             if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                            std::vector<std::size_t>({stride, 1}));
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
             }
             else
             {
-                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                            std::vector<std::size_t>({1, stride}));
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
             }
         };
 
@@ -208,10 +208,10 @@ bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& co
 
 #ifdef BUILD_INT4_EXAMPLE
             const Tensor<EDataType> c_device_result_converted(c_device_tensors[i]);
-            pass &= ck::utils::check_err(c_device_result_converted.mData, c_host_tensors[i].mData);
+            pass &= ck::utils::check_err(c_device_result_converted, c_host_tensors[i]);
 
 #else
-            pass &= ck::utils::check_err(c_device_tensors[i].mData, c_host_tensors[i].mData);
+            pass &= ck::utils::check_err(c_device_tensors[i], c_host_tensors[i]);
 #endif
         }
     }

example/16_gemm_multi_d_multi_reduces/gemm_add_add_mean_meansquare_xdl_fp16.cpp

@@ -15,6 +15,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
 #include "ck/library/utility/check_err.hpp"
 
@@ -109,21 +110,20 @@ void DumpPerf(float ave_time, int M, int N, int K)
 }
 
 auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
-    return HostTensorDescriptor(std::vector<std::size_t>({len}),
-                                std::vector<std::size_t>({stride}));
+    return HostTensorDescriptor({len}, {stride});
 };
 
 auto f_host_tensor_descriptor2d =
     [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+        using namespace ck::literals;
+
         if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                        std::vector<std::size_t>({stride, 1}));
+            return HostTensorDescriptor({row, col}, {stride, 1_uz});
         }
         else
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                        std::vector<std::size_t>({1, stride}));
+            return HostTensorDescriptor({row, col}, {1_uz, stride});
         }
     };
 
@@ -259,12 +259,9 @@ int main()
         r0_device_buf.FromDevice(r0_m.mData.data());
         r1_device_buf.FromDevice(r1_m.mData.data());
 
-        pass = ck::utils::check_err(
-            e_m_n.mData, e_m_n_host.mData, "Error: Incorrect results c", 1e-2, 1e-2);
-        pass &= ck::utils::check_err(
-            r0_m.mData, r0_m_host.mData, "Error: Incorrect results d0", 1e-2, 1e-2);
-        pass &= ck::utils::check_err(
-            r1_m.mData, r1_m_host.mData, "Error: Incorrect results d1", 1e-2, 1e-2);
+        pass = ck::utils::check_err(e_m_n, e_m_n_host, "Error: Incorrect results c", 1e-2, 1e-2);
+        pass &= ck::utils::check_err(r0_m, r0_m_host, "Error: Incorrect results d0", 1e-2, 1e-2);
+        pass &= ck::utils::check_err(r1_m, r1_m_host, "Error: Incorrect results d1", 1e-2, 1e-2);
     }
 
     bool time_kernel = true;

example/16_gemm_multi_d_multi_reduces/gemm_add_addsquare_xdl_int8.cpp

@@ -160,14 +160,12 @@ bool run_gemm_reduce_add_addsquare_xdl(ck::index_t M,
     {
     case 0: break;
     case 1:
-        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k.begin(),
-                                                                             a_m_k.end());
-        ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n.begin(),
-                                                                             b_k_n.end());
+        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
+        ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n);
         break;
     default:
-        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k.begin(), a_m_k.end());
-        ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n.begin(), b_k_n.end());
+        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
+        ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
         break;
     }
 
@@ -264,15 +262,13 @@ bool run_gemm_reduce_add_addsquare_xdl(ck::index_t M,
         Tensor<EDataType> e_m_n_host_converted(e_m_n_host);
 
         pass = ck::utils::check_err(
-            e_m_n.mData, e_m_n_host_converted.mData, "Error: Incorrect results c", 1e-2, 1e-2);
+            e_m_n, e_m_n_host_converted, "Error: Incorrect results c", 1e-2, 1e-2);
 
         r0_device_buf.FromDevice(r0_m.mData.data());
         r1_device_buf.FromDevice(r1_m.mData.data());
 
-        pass &= ck::utils::check_err(
-            r0_m.mData, r0_m_host.mData, "Error: Incorrect results d0", 1e-2, 1e-2);
-        pass &= ck::utils::check_err(
-            r1_m.mData, r1_m_host.mData, "Error: Incorrect results d1", 1e-2, 1e-2);
+        pass &= ck::utils::check_err(r0_m, r0_m_host, "Error: Incorrect results d0", 1e-2, 1e-2);
+        pass &= ck::utils::check_err(r1_m, r1_m_host, "Error: Incorrect results d1", 1e-2, 1e-2);
 
         if(pass)
         {

example/16_gemm_multi_d_multi_reduces/gemm_reduce_xdl_common.hpp

@@ -134,14 +134,12 @@ auto run_gemm_reduce_max_xdl(ck::index_t M,
     {
     case 0: break;
     case 1:
-        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k.begin(),
-                                                                             a_m_k.end());
-        ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n.begin(),
-                                                                             b_k_n.end());
+        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
+        ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n);
         break;
     default:
-        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k.begin(), a_m_k.end());
-        ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n.begin(), b_k_n.end());
+        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
+        ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
         break;
     }
 
@@ -243,8 +241,8 @@ auto run_gemm_reduce_max_xdl(ck::index_t M,
         if constexpr(std::is_same_v<ADataType, ck::int4_t>)
         {
             Tensor<EDataType> e_m_n_device_converted(e_m_n);
-            pass = ck::utils::check_err(e_m_n_device_converted.mData,
-                                        e_m_n_host_converted.mData,
+            pass = ck::utils::check_err(e_m_n_device_converted,
+                                        e_m_n_host_converted,
                                         "Error: Incorrect results c",
                                         1e-2,
                                         1e-2);
@@ -253,12 +251,11 @@ auto run_gemm_reduce_max_xdl(ck::index_t M,
 #endif // CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
         {
             pass = ck::utils::check_err(
-                e_m_n.mData, e_m_n_host_converted.mData, "Error: Incorrect results c", 1e-2, 1e-2);
+                e_m_n, e_m_n_host_converted, "Error: Incorrect results c", 1e-2, 1e-2);
         }
 
         r0_device_buf.FromDevice(r0_m.mData.data());
-        pass &= ck::utils::check_err(
-            r0_m.mData, r0_m_host.mData, "Error: Incorrect results d0", 1e-2, 1e-2);
+        pass &= ck::utils::check_err(r0_m, r0_m_host, "Error: Incorrect results d0", 1e-2, 1e-2);
 
         if(pass)
         {
@@ -339,14 +336,12 @@ bool run_gemm_reduce_mean_meansquare_xdl(ck::index_t M,
     {
     case 0: break;
     case 1:
-        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k.begin(),
-                                                                             a_m_k.end());
-        ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n.begin(),
-                                                                             b_k_n.end());
+        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
+        ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n);
         break;
     default:
-        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k.begin(), a_m_k.end());
-        ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n.begin(), b_k_n.end());
+        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
+        ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
         break;
     }
 
@@ -460,8 +455,8 @@ bool run_gemm_reduce_mean_meansquare_xdl(ck::index_t M,
         if constexpr(std::is_same_v<ADataType, ck::int4_t>)
         {
             Tensor<EDataType> e_m_n_device_converted(e_m_n);
-            pass = ck::utils::check_err(e_m_n_device_converted.mData,
-                                        e_m_n_host_converted.mData,
+            pass = ck::utils::check_err(e_m_n_device_converted,
+                                        e_m_n_host_converted,
                                         "Error: Incorrect results c",
                                         1e-2,
                                         1e-2);
@@ -470,16 +465,14 @@ bool run_gemm_reduce_mean_meansquare_xdl(ck::index_t M,
 #endif // CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
         {
             pass = ck::utils::check_err(
-                e_m_n.mData, e_m_n_host_converted.mData, "Error: Incorrect results c", 1e-2, 1e-2);
+                e_m_n, e_m_n_host_converted, "Error: Incorrect results c", 1e-2, 1e-2);
         }
 
         r0_device_buf.FromDevice(r0_m.mData.data());
         r1_device_buf.FromDevice(r1_m.mData.data());
 
-        pass &= ck::utils::check_err(
-            r0_m.mData, r0_m_host.mData, "Error: Incorrect results d0", 1e-2, 1e-2);
-        pass &= ck::utils::check_err(
-            r1_m.mData, r1_m_host.mData, "Error: Incorrect results d1", 1e-2, 1e-2);
+        pass &= ck::utils::check_err(r0_m, r0_m_host, "Error: Incorrect results d0", 1e-2, 1e-2);
+        pass &= ck::utils::check_err(r1_m, r1_m_host, "Error: Incorrect results d1", 1e-2, 1e-2);
 
         if(pass)
         {

example/17_convnd_bwd_data/convnd_bwd_data_common.hpp

@@ -142,7 +142,7 @@ int run_conv_bwd_data(bool do_verification,
 
         in_device_buf.FromDevice(in_device.mData.data());
 
-        return ck::utils::check_err(in_device.mData, in_host.mData) ? 0 : 1;
+        return ck::utils::check_err(in_device, in_host) ? 0 : 1;
     }
 
     return 0;

example/18_batched_gemm_reduce/batched_gemm_reduce_xdl_fp16.cpp

@@ -16,6 +16,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
 
 template <ck::index_t... Is>
@@ -132,15 +133,15 @@ int main(int argc, char* argv[])
                                        std::size_t col,
                                        std::size_t stride,
                                        auto layout) {
+        using namespace ck::literals;
+
         if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
-                                        std::vector<std::size_t>({row * stride, stride, 1}));
+            return HostTensorDescriptor({batch_count, row, col}, {row * stride, stride, 1_uz});
         }
         else
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
-                                        std::vector<std::size_t>({col * stride, 1, stride}));
+            return HostTensorDescriptor({batch_count, row, col}, {col * stride, 1_uz, stride});
         }
     };
 
@@ -149,17 +150,13 @@ int main(int argc, char* argv[])
 
     Tensor<CDataType> c_g_m_n_host_result(
         f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
-    Tensor<ReduceDataType> d0_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
-        {static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
-    Tensor<ReduceDataType> d1_g_m_host_result(HostTensorDescriptor(std::vector<std::size_t>(
-        {static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
+    Tensor<ReduceDataType> d0_g_m_host_result({BatchCount, M});
+    Tensor<ReduceDataType> d1_g_m_host_result({BatchCount, M});
 
     Tensor<CDataType> c_g_m_n_device_result(
         f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
-    Tensor<ReduceDataType> d0_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
-        {static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
-    Tensor<ReduceDataType> d1_g_m_device_result(HostTensorDescriptor(std::vector<std::size_t>(
-        {static_cast<std::size_t>(BatchCount), static_cast<std::size_t>(M)})));
+    Tensor<ReduceDataType> d0_g_m_device_result({BatchCount, M});
+    Tensor<ReduceDataType> d1_g_m_device_result({BatchCount, M});
 
     std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
     std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
@@ -296,16 +293,15 @@ int main(int argc, char* argv[])
             }
         }
 
-        pass = ck::utils::check_err(c_g_m_n_host_result.mData,
-                                    c_g_m_n_device_result.mData,
-                                    "Error: Incorrect results c") &&
-               ck::utils::check_err(d0_g_m_device_result.mData,
-                                    d0_g_m_host_result.mData,
+        pass = ck::utils::check_err(
+                   c_g_m_n_host_result, c_g_m_n_device_result, "Error: Incorrect results c") &&
+               ck::utils::check_err(d0_g_m_device_result,
+                                    d0_g_m_host_result,
                                     "Error: Incorrect results! D0",
                                     1e-4,
                                     1e-5) &&
-               ck::utils::check_err(d1_g_m_device_result.mData,
-                                    d1_g_m_host_result.mData,
+               ck::utils::check_err(d1_g_m_device_result,
+                                    d1_g_m_host_result,
                                     "Error: Incorrect results! D1",
                                     1e-3,
                                     1e-5);

example/19_binary_elementwise/broadcast_add_2d_amn_bn.cpp

@@ -12,6 +12,7 @@
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
 #include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/literals.hpp"
 
 using F16 = ck::half_t;
 using F32 = float;
@@ -71,13 +72,13 @@ int main()
     ck::index_t Stride = 1024;
 
     auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
-        return HostTensorDescriptor(std::vector<std::size_t>({len}),
-                                    std::vector<std::size_t>({stride}));
+        return HostTensorDescriptor({len}, {stride});
     };
 
     auto f_host_tensor_descriptor2d = [](std::size_t row, std::size_t col, std::size_t stride) {
-        return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
-                                    std::vector<std::size_t>({stride, 1}));
+        using namespace ck::literals;
+
+        return HostTensorDescriptor({row, col}, {stride, 1_uz});
     };
 
     Tensor<ABDataType> a_m_n(f_host_tensor_descriptor2d(M, N, Stride));
@@ -128,8 +129,7 @@ int main()
         host_broadcast2D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add, 0>(
             host_c_m_n, a_m_n, b_n, M, N, Add{});
 
-        pass &= ck::utils::check_err(
-            c_m_n.mData, host_c_m_n.mData, "Error: Incorrect results c", 1e-3, 1e-3);
+        pass &= ck::utils::check_err(c_m_n, host_c_m_n, "Error: Incorrect results c", 1e-3, 1e-3);
     }
 
     return pass ? 0 : 1;

example/19_binary_elementwise/broadcast_add_3d_am_bmnk.cpp

@@ -8,6 +8,7 @@
 #include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
 
+#include "ck/library/utility/algorithm.hpp"
 #include "ck/library/utility/check_err.hpp"
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
@@ -82,11 +83,9 @@ int main()
     std::array<ck::index_t, 3> b_strides;
     std::array<ck::index_t, 3> c_strides;
 
-    std::copy(mnk.begin(), mnk.end(), abc_lengths.begin());
-    std::copy(
-        b_m_n_k.mDesc.GetStrides().begin(), b_m_n_k.mDesc.GetStrides().end(), b_strides.begin());
-    std::copy(
-        c_m_n_k.mDesc.GetStrides().begin(), c_m_n_k.mDesc.GetStrides().end(), c_strides.begin());
+    ck::ranges::copy(mnk, abc_lengths.begin());
+    ck::ranges::copy(b_m_n_k.mDesc.GetStrides(), b_strides.begin());
+    ck::ranges::copy(c_m_n_k.mDesc.GetStrides(), c_strides.begin());
 
     auto broadcastAdd = DeviceElementwiseAddInstance{};
     auto argument     = broadcastAdd.MakeArgumentPointer(
@@ -113,8 +112,8 @@ int main()
         host_broadcast3D_am_bmnk<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
             host_c_m_n_k, a_m, b_m_n_k, mnk, Add{});
 
-        pass &= ck::utils::check_err(
-            c_m_n_k.mData, host_c_m_n_k.mData, "Error: Incorrect results c", 1e-3, 1e-3);
+        pass &=
+            ck::utils::check_err(c_m_n_k, host_c_m_n_k, "Error: Incorrect results c", 1e-3, 1e-3);
     }
 
     return pass ? 0 : 1;

example/19_binary_elementwise/elementwise_add_1d.cpp

@@ -53,8 +53,7 @@ int main()
     ck::index_t M = 1024;
 
     auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
-        return HostTensorDescriptor(std::vector<std::size_t>({len}),
-                                    std::vector<std::size_t>({stride}));
+        return HostTensorDescriptor({len}, {stride});
     };
 
     Tensor<ABDataType> a_m(f_host_tensor_descriptor1d(M, 1));
@@ -105,8 +104,7 @@ int main()
         host_elementwise1D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
             host_c_m, a_m, b_m, M, Add{});
 
-        pass &= ck::utils::check_err(
-            c_m.mData, host_c_m.mData, "Error: Incorrect results c", 1e-3, 1e-3);
+        pass &= ck::utils::check_err(c_m, host_c_m, "Error: Incorrect results c", 1e-3, 1e-3);
     }
 
     return pass ? 0 : 1;

example/19_binary_elementwise/elementwise_add_4d.cpp

@@ -8,6 +8,7 @@
 #include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
 
+#include "ck/library/utility/algorithm.hpp"
 #include "ck/library/utility/check_err.hpp"
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/host_tensor.hpp"
@@ -82,10 +83,10 @@ int main()
     std::array<ck::index_t, 4> b_strides;
     std::array<ck::index_t, 4> c_strides;
 
-    std::copy(nchw.begin(), nchw.end(), abc_lengths.begin());
-    std::copy(a.mDesc.GetStrides().begin(), a.mDesc.GetStrides().end(), a_strides.begin());
-    std::copy(b.mDesc.GetStrides().begin(), b.mDesc.GetStrides().end(), b_strides.begin());
-    std::copy(c.mDesc.GetStrides().begin(), c.mDesc.GetStrides().end(), c_strides.begin());
+    ck::ranges::copy(nchw, abc_lengths.begin());
+    ck::ranges::copy(a.mDesc.GetStrides(), a_strides.begin());
+    ck::ranges::copy(b.mDesc.GetStrides(), b_strides.begin());
+    ck::ranges::copy(c.mDesc.GetStrides(), c_strides.begin());
 
     auto broadcastAdd = DeviceElementwiseAddInstance{};
     auto argument     = broadcastAdd.MakeArgumentPointer(
@@ -112,8 +113,7 @@ int main()
         host_elementwise4D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
             host_c, a, b, nchw, Add{});
 
-        pass &=
-            ck::utils::check_err(c.mData, host_c.mData, "Error: Incorrect results c", 1e-3, 1e-3);
+        pass &= ck::utils::check_err(c, host_c, "Error: Incorrect results c", 1e-3, 1e-3);
     }
 
     return pass ? 0 : 1;

example/20_convnd_bwd_weight/CMakeLists.txt

-add_example_executable(example_convnd_bwd_weight_xdl_fp16 convnd_bwd_weight_xdl_fp16.cpp)
-add_example_executable(example_convnd_bwd_weight_xdl_bf16 convnd_bwd_weight_xdl_bf16.cpp)
-
-target_link_libraries(example_convnd_bwd_weight_xdl_fp16 PRIVATE utility)
-target_link_libraries(example_convnd_bwd_weight_xdl_bf16 PRIVATE utility)

example/20_convnd_bwd_weight/convnd_bwd_weight_xdl_bf16.cpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
-
-#include "convnd_bwd_weight_common.hpp"
-
-#include "ck/tensor_operation/gpu/device/impl/device_convnd_bwd_weight_nwc_kxc_nwk_xdl_cshuffle.hpp"
-
-using InDataType = ck::bhalf_t;
-// bf16 kernel use fp32 atomic add to accumulate Weight tensor into global memory
-using WeiDataType = float;
-using OutDataType = ck::bhalf_t;
-using AccDataType = float;
-
-template <ck::index_t... Is>
-using S = ck::Sequence<Is...>;
-
-using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
-using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
-using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
-
-static constexpr auto ConvBwdWeightDefault =
-    ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
-
-template <ck::index_t NDimSpatial>
-using DeviceConvndBwdWeightInstance =
-    ck::tensor_operation::device::DeviceConvNdBwdWeightNwcKxcNwk_Xdl_CShuffle<
-        NDimSpatial,          // NDimSpatial
-        InDataType,           // InDataType
-        WeiDataType,          // WeiDataType
-        OutDataType,          // OutDataType
-        AccDataType,          // AccDataType
-        InElementOp,          // InElementwiseOperation
-        WeiElementOp,         // WeiElementwiseOperation
-        OutElementOp,         // OutElementwiseOperation
-        ConvBwdWeightDefault, // ConvolutionBackwardWeightSpecialization
-        256,                  // BlockSize
-        128,                  // MPerBlock
-        128,                  // NPerBlock
-        4,                    // K0PerBlock
-        8,                    // K1
-        32,                   // MPerXdl
-        32,                   // NPerXdl
-        2,                    // MXdlPerWave
-        2,                    // NXdlPerWave
-        S<1, 4, 16, 4>,       // ABlockTransferThreadClusterLengths_K0_M_K1
-        S<0, 3, 1, 2>,        // ABlockTransferThreadClusterArrangeOrder
-        S<0, 2, 1, 3>,        // ABlockTransferSrcAccessOrder
-        2,                    // ABlockTransferSrcVectorDim
-        8,                    // ABlockTransferSrcScalarPerVector
-        2,                    // ABlockTransferDstScalarPerVector_K1
-        true,                 // ABlockLdsAddExtraM
-        S<1, 4, 16, 4>,       // BBlockTransferThreadClusterLengths_K0_N_K1
-        S<0, 3, 1, 2>,        // BBlockTransferThreadClusterArrangeOrder
-        S<0, 2, 1, 3>,        // BBlockTransferSrcAccessOrder
-        2,                    // BBlockTransferSrcVectorDim
-        8,                    // BBlockTransferSrcScalarPerVector
-        2,                    // BBlockTransferDstScalarPerVector_K1
-        true,                 // BBlockLdsAddExtraN
-        1,                    // CShuffleMXdlPerWavePerShuffle
-        1,                    // CShuffleNXdlPerWavePerShuffle
-        S<1, 32, 1, 4>,       // CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
-        4>;                   // CBlockTransferScalarPerVector_NWaveNPerXdl
-
-int main(int argc, char* argv[])
-{
-    namespace ctc = ck::tensor_layout::convolution;
-
-    print_helper_msg();
-
-    bool do_verification = true;
-    int init_method      = 1;
-    bool time_kernel     = false;
-
-    ck::utils::conv::ConvParam conv_param{
-        2, 1, 32, 256, 1024, {3, 3}, {14, 14}, {2, 2}, {1, 1}, {1, 1}, {1, 1}};
-
-    ck::index_t split_k = 4;
-
-    if(argc == 1)
-    {
-        // use default
-    }
-    else if(argc == 4)
-    {
-        do_verification = std::stoi(argv[1]);
-        init_method     = std::stoi(argv[2]);
-        time_kernel     = std::stoi(argv[3]);
-    }
-    else
-    {
-        do_verification                   = std::stoi(argv[1]);
-        init_method                       = std::stoi(argv[2]);
-        time_kernel                       = std::stoi(argv[3]);
-        const ck::index_t num_dim_spatial = std::stoi(argv[4]);
-
-        conv_param = ck::utils::conv::parse_conv_param(num_dim_spatial, 5, argv);
-
-        split_k = std::stoi(argv[5 + 3 + 6 * num_dim_spatial - 1]);
-        split_k = std::max(1, split_k);
-    }
-
-    const auto in_element_op  = InElementOp{};
-    const auto wei_element_op = WeiElementOp{};
-    const auto out_element_op = OutElementOp{};
-
-    if(conv_param.num_dim_spatial_ == 1)
-    {
-        using InLayout  = ctc::GNWC;
-        using WeiLayout = ctc::GKXC;
-        using OutLayout = ctc::GNWK;
-
-        const auto in_g_n_c_wis_desc =
-            ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
-                conv_param);
-
-        const auto wei_g_k_c_xs_desc =
-            ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
-                conv_param);
-
-        const auto out_g_n_k_wos_desc =
-            ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
-                conv_param);
-
-        return run_conv_bwd_weight<1,
-                                   InDataType,
-                                   WeiDataType,
-                                   OutDataType,
-                                   InElementOp,
-                                   WeiElementOp,
-                                   OutElementOp,
-                                   DeviceConvndBwdWeightInstance<1>>(do_verification,
-                                                                     init_method,
-                                                                     time_kernel,
-                                                                     conv_param,
-                                                                     in_g_n_c_wis_desc,
-                                                                     wei_g_k_c_xs_desc,
-                                                                     out_g_n_k_wos_desc,
-                                                                     in_element_op,
-                                                                     wei_element_op,
-                                                                     out_element_op,
-                                                                     split_k);
-    }
-    else if(conv_param.num_dim_spatial_ == 2)
-    {
-        using InLayout  = ctc::GNHWC;
-        using WeiLayout = ctc::GKYXC;
-        using OutLayout = ctc::GNHWK;
-
-        const auto in_g_n_c_wis_desc =
-            ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
-                conv_param);
-
-        const auto wei_g_k_c_xs_desc =
-            ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
-                conv_param);
-
-        const auto out_g_n_k_wos_desc =
-            ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
-                conv_param);
-
-        return run_conv_bwd_weight<2,
-                                   InDataType,
-                                   WeiDataType,
-                                   OutDataType,
-                                   InElementOp,
-                                   WeiElementOp,
-                                   OutElementOp,
-                                   DeviceConvndBwdWeightInstance<2>>(do_verification,
-                                                                     init_method,
-                                                                     time_kernel,
-                                                                     conv_param,
-                                                                     in_g_n_c_wis_desc,
-                                                                     wei_g_k_c_xs_desc,
-                                                                     out_g_n_k_wos_desc,
-                                                                     in_element_op,
-                                                                     wei_element_op,
-                                                                     out_element_op,
-                                                                     split_k);
-    }
-    else if(conv_param.num_dim_spatial_ == 3)
-    {
-        using InLayout  = ctc::GNDHWC;
-        using WeiLayout = ctc::GKZYXC;
-        using OutLayout = ctc::GNDHWK;
-
-        const auto in_g_n_c_wis_desc =
-            ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
-                conv_param);
-
-        const auto wei_g_k_c_xs_desc =
-            ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
-                conv_param);
-
-        const auto out_g_n_k_wos_desc =
-            ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
-                conv_param);
-
-        return run_conv_bwd_weight<3,
-                                   InDataType,
-                                   WeiDataType,
-                                   OutDataType,
-                                   InElementOp,
-                                   WeiElementOp,
-                                   OutElementOp,
-                                   DeviceConvndBwdWeightInstance<3>>(do_verification,
-                                                                     init_method,
-                                                                     time_kernel,
-                                                                     conv_param,
-                                                                     in_g_n_c_wis_desc,
-                                                                     wei_g_k_c_xs_desc,
-                                                                     out_g_n_k_wos_desc,
-                                                                     in_element_op,
-                                                                     wei_element_op,
-                                                                     out_element_op,
-                                                                     split_k);
-    }
-
-    return 0;
-}