Rangify constructor of HostTensorDescriptor & Tensor<> (#445)

* Rangify STL algorithms

This commit adapts rangified std::copy(), std::fill() & std::transform()

* Rangify check_err()

By rangifying check_err(), we can not only compare values between
std::vector<>s, but also compare any ranges which have same value
type.

* Allow constructing Tensor<> like a HostTensorDescriptor

* Simplify Tensor<> object construction logics

* Remove more unnecessary 'HostTensorDescriptor' objects

* Re-format example code

* Re-write more HostTensorDescriptor ctor call

example/38_grouped_conv_bwd_data_multiple_d/run_grouped_conv_bwd_data_bias_relu_example.inc

@@ -61,7 +61,7 @@ bool run_conv_bwd_data_bias_relu(const ExecutionConfig& config,
     std::array<ck::index_t, NDimSpatial> input_left_pads{};
     std::array<ck::index_t, NDimSpatial> input_right_pads{};
 
-    auto copy = [](auto& x, auto& y) { std::copy(x.begin(), x.end(), y.begin()); };
+    auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
 
     copy(out_g_n_k_wos_desc.GetLengths(), a_g_n_k_wos_lengths);
     copy(out_g_n_k_wos_desc.GetStrides(), a_g_n_k_wos_strides);
@@ -157,7 +157,7 @@ bool run_conv_bwd_data_bias_relu(const ExecutionConfig& config,
 
         in_device_buf.FromDevice(in_device.mData.data());
 
-        return ck::utils::check_err(in_device.mData, in_host.mData);
+        return ck::utils::check_err(in_device, in_host);
     }
 
     return true;

example/39_permute/common.hpp

@@ -19,6 +19,7 @@
 #include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
 #include "ck/utility/type.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/fill.hpp"
@@ -247,19 +248,6 @@ inline auto to_array(Range& range) noexcept
     return detail::to_array_proxy<ck::remove_cvref_t<Range>>{range};
 }
 
-namespace ranges {
-template <typename InputRange, typename OutputIterator>
-inline auto copy(InputRange&& range, OutputIterator iter)
-    -> decltype(std::copy(std::begin(std::forward<InputRange>(range)),
-                          std::end(std::forward<InputRange>(range)),
-                          iter))
-{
-    return std::copy(std::begin(std::forward<InputRange>(range)),
-                     std::end(std::forward<InputRange>(range)),
-                     iter);
-}
-} // namespace ranges
-
 template <typename Axes>
 inline auto is_valid_axes(const Axes& axes)
     -> std::enable_if_t<detail::is_random_access_range_v<Axes>, bool>
@@ -350,7 +338,7 @@ auto extend_shape(const Problem::Shape& shape, std::size_t new_dim)
 
     using std::begin, std::end;
 
-    std::copy(begin(shape), end(shape), begin(extended_shape));
+    ck::ranges::copy(shape, begin(extended_shape));
     extended_shape.back() = new_dim;
 
     return extended_shape;
@@ -362,7 +350,7 @@ auto extend_axes(const Problem::Axes& axes)
 
     using std::begin, std::end;
 
-    std::copy(begin(axes), end(axes), begin(extended_axes));
+    ck::ranges::copy(axes, begin(extended_axes));
     extended_axes.back() = detail::get_array_size_v<Problem::Axes>;
 
     return extended_axes;

example/39_permute/run_permute_bundle_example.inc

@@ -57,7 +57,7 @@ bool run_permute_bundle(const Problem& problem)
     using std::begin;
 
     Tensor<DataType> input_tensor(input_shape);
-    ranges::copy(input_bundle_tensor.AsSpan<const DataType>(), begin(input_tensor));
+    ck::ranges::copy(input_bundle_tensor.AsSpan<const DataType>(), begin(input_tensor));
 
     Tensor<DataType> output_tensor(transpose(input_shape, input_axes));
     if(!host_permute(input_tensor, input_axes, PassThrough{}, output_tensor))

example/41_grouped_conv_conv_fwd/grouped_conv_conv_fwd_xdl_bf16.cpp

@@ -11,6 +11,7 @@
 #include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.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"

example/41_grouped_conv_conv_fwd/grouped_conv_conv_fwd_xdl_fp16.cpp

@@ -11,6 +11,7 @@
 #include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.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"

example/41_grouped_conv_conv_fwd/grouped_conv_conv_fwd_xdl_fp32.cpp

@@ -11,6 +11,7 @@
 #include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.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"

example/41_grouped_conv_conv_fwd/grouped_conv_conv_fwd_xdl_int4.cpp

@@ -15,6 +15,7 @@
 #include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.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"

example/41_grouped_conv_conv_fwd/grouped_conv_conv_fwd_xdl_int8.cpp

@@ -11,6 +11,7 @@
 #include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_gemm_xdl_cshuffle.hpp"
 #include "ck/tensor_operation/gpu/element/element_wise_operation.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"

example/41_grouped_conv_conv_fwd/run_grouped_conv_conv_fwd_example.inc

@@ -97,7 +97,7 @@ bool run_grouped_conv_conv_fwd(bool do_verification,
     std::array<ck::index_t, NDimSpatial> input1_left_pads{};
     std::array<ck::index_t, NDimSpatial> input1_right_pads{};
 
-    auto copy = [](auto& x, auto& y) { std::copy(x.begin(), x.end(), y.begin()); };
+    auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
 
     copy(in0_g_n_c_wis_desc.GetLengths(), a0_g_n_c_wis_lengths);
     copy(in0_g_n_c_wis_desc.GetStrides(), a0_g_n_c_wis_strides);
@@ -261,7 +261,7 @@ bool run_grouped_conv_conv_fwd(bool do_verification,
 #endif
 
         return ck::utils::check_err(
-            out1_device.mData, out1_host.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
+            out1_device, out1_host, "Error: incorrect results!", 1e-5f, 1e-4f);
     }
 
     return true;

example/42_groupnorm/groupnorm_sigmoid_fp16.cpp

@@ -167,7 +167,7 @@ int main(int argc, char* argv[])
         ref_invoker.Run(ref_argument);
 
         y_dev.FromDevice(y.mData.data());
-        pass &= ck::utils::check_err(y.mData, host_y.mData, "Error: Incorrect results", 1e-3, 1e-3);
+        pass &= ck::utils::check_err(y, host_y, "Error: Incorrect results", 1e-3, 1e-3);
     }
 
     return (pass ? 0 : 1);

library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_layernorm.hpp

@@ -44,8 +44,8 @@ struct ReferenceGemmLayernorm : public device::BaseOperator
         size_t M = acc.mDesc.GetLengths()[0];
         size_t N = acc.mDesc.GetLengths()[1];
 
-        Tensor<ComputeDataType> avg_acc_sq(HostTensorDescriptor(std::vector<size_t>({M})));
-        Tensor<ComputeDataType> avg_acc(HostTensorDescriptor(std::vector<size_t>({M})));
+        Tensor<ComputeDataType> avg_acc_sq({M});
+        Tensor<ComputeDataType> avg_acc({M});
         Tensor<ComputeDataType> acc_layernorm(acc);
 
         // reduce N dim

library/include/ck/library/utility/algorithm.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <algorithm>
+#include <iterator>
+#include <type_traits>
+#include <utility>
+
+namespace ck {
+namespace ranges {
+template <typename InputRange, typename OutputIterator>
+auto copy(InputRange&& range, OutputIterator iter)
+    -> decltype(std::copy(std::begin(std::forward<InputRange>(range)),
+                          std::end(std::forward<InputRange>(range)),
+                          iter))
+{
+    return std::copy(std::begin(std::forward<InputRange>(range)),
+                     std::end(std::forward<InputRange>(range)),
+                     iter);
+}
+
+template <typename T, typename OutputRange>
+auto fill(OutputRange&& range, const T& init)
+    -> std::void_t<decltype(std::fill(std::begin(std::forward<OutputRange>(range)),
+                                      std::end(std::forward<OutputRange>(range)),
+                                      init))>
+{
+    std::fill(std::begin(std::forward<OutputRange>(range)),
+              std::end(std::forward<OutputRange>(range)),
+              init);
+}
+
+template <typename InputRange, typename OutputIterator, typename UnaryOperation>
+auto transform(InputRange&& range, OutputIterator iter, UnaryOperation unary_op)
+    -> decltype(std::transform(std::begin(range), std::end(range), iter, unary_op))
+{
+    return std::transform(std::begin(range), std::end(range), iter, unary_op);
+}
+
+} // namespace ranges
+} // namespace ck

library/include/ck/library/utility/check_err.hpp

@@ -15,18 +15,22 @@
 
 #include "ck/ck.hpp"
 #include "ck/utility/data_type.hpp"
-#include "ck/utility/span.hpp"
 #include "ck/utility/type.hpp"
 #include "ck/host_utility/io.hpp"
 
+#include "ck/library/utility/ranges.hpp"
+
 namespace ck {
 namespace utils {
 
-template <typename T>
-typename std::enable_if<std::is_floating_point<T>::value && !std::is_same<T, half_t>::value,
-                        bool>::type
-check_err(const std::vector<T>& out,
-          const std::vector<T>& ref,
+template <typename Range, typename RefRange>
+typename std::enable_if<
+    std::is_same_v<ranges::range_value_t<Range>, ranges::range_value_t<RefRange>> &&
+        std::is_floating_point_v<ranges::range_value_t<Range>> &&
+        !std::is_same_v<ranges::range_value_t<Range>, half_t>,
+    bool>::type
+check_err(const Range& out,
+          const RefRange& ref,
           const std::string& msg = "Error: Incorrect results!",
           double rtol            = 1e-5,
           double atol            = 3e-6)
@@ -44,15 +48,17 @@ check_err(const std::vector<T>& out,
     double max_err = std::numeric_limits<double>::min();
     for(std::size_t i = 0; i < ref.size(); ++i)
     {
-        err = std::abs(out[i] - ref[i]);
-        if(err > atol + rtol * std::abs(ref[i]) || !std::isfinite(out[i]) || !std::isfinite(ref[i]))
+        const double o = *std::next(std::begin(out), i);
+        const double r = *std::next(std::begin(ref), i);
+        err            = std::abs(o - r);
+        if(err > atol + rtol * std::abs(r) || !std::isfinite(o) || !std::isfinite(r))
         {
             max_err = err > max_err ? err : max_err;
             err_count++;
             if(err_count < 5)
             {
                 std::cerr << msg << std::setw(12) << std::setprecision(7) << " out[" << i
-                          << "] != ref[" << i << "]: " << out[i] << " != " << ref[i] << std::endl;
+                          << "] != ref[" << i << "]: " << o << " != " << r << std::endl;
             }
             res = false;
         }
@@ -64,10 +70,13 @@ check_err(const std::vector<T>& out,
     return res;
 }
 
-template <typename T>
-typename std::enable_if<std::is_same<T, bhalf_t>::value, bool>::type
-check_err(const std::vector<T>& out,
-          const std::vector<T>& ref,
+template <typename Range, typename RefRange>
+typename std::enable_if<
+    std::is_same_v<ranges::range_value_t<Range>, ranges::range_value_t<RefRange>> &&
+        std::is_same_v<ranges::range_value_t<Range>, bhalf_t>,
+    bool>::type
+check_err(const Range& out,
+          const RefRange& ref,
           const std::string& msg = "Error: Incorrect results!",
           double rtol            = 1e-3,
           double atol            = 1e-3)
@@ -86,9 +95,9 @@ check_err(const std::vector<T>& out,
     double max_err = std::numeric_limits<float>::min();
     for(std::size_t i = 0; i < ref.size(); ++i)
     {
-        double o = type_convert<float>(out[i]);
-        double r = type_convert<float>(ref[i]);
-        err      = std::abs(o - r);
+        const double o = type_convert<float>(*std::next(std::begin(out), i));
+        const double r = type_convert<float>(*std::next(std::begin(ref), i));
+        err            = std::abs(o - r);
         if(err > atol + rtol * std::abs(r) || !std::isfinite(o) || !std::isfinite(r))
         {
             max_err = err > max_err ? err : max_err;
@@ -108,10 +117,13 @@ check_err(const std::vector<T>& out,
     return res;
 }
 
-template <typename T>
-typename std::enable_if<std::is_same_v<T, half_t>, bool>::type
-check_err(span<const T> out,
-          span<const T> ref,
+template <typename Range, typename RefRange>
+typename std::enable_if<
+    std::is_same_v<ranges::range_value_t<Range>, ranges::range_value_t<RefRange>> &&
+        std::is_same_v<ranges::range_value_t<Range>, half_t>,
+    bool>::type
+check_err(const Range& out,
+          const RefRange& ref,
           const std::string& msg = "Error: Incorrect results!",
           double rtol            = 1e-3,
           double atol            = 1e-3)
@@ -126,12 +138,12 @@ check_err(span<const T> out,
     bool res{true};
     int err_count  = 0;
     double err     = 0;
-    double max_err = std::numeric_limits<T>::min();
+    double max_err = std::numeric_limits<ranges::range_value_t<Range>>::min();
     for(std::size_t i = 0; i < ref.size(); ++i)
     {
-        double o = type_convert<float>(out[i]);
-        double r = type_convert<float>(ref[i]);
-        err      = std::abs(o - r);
+        const double o = type_convert<float>(*std::next(std::begin(out), i));
+        const double r = type_convert<float>(*std::next(std::begin(ref), i));
+        err            = std::abs(o - r);
         if(err > atol + rtol * std::abs(r) || !std::isfinite(o) || !std::isfinite(r))
         {
             max_err = err > max_err ? err : max_err;
@@ -151,26 +163,17 @@ check_err(span<const T> out,
     return res;
 }
 
-template <typename T>
-typename std::enable_if<std::is_same<T, half_t>::value, bool>::type
-check_err(const std::vector<T>& out,
-          const std::vector<T>& ref,
-          const std::string& msg = "Error: Incorrect results!",
-          double rtol            = 1e-3,
-          double atol            = 1e-3)
-{
-    return check_err(span<const T>{out}, span<const T>{ref}, msg, rtol, atol);
-}
-
-template <typename T>
-std::enable_if_t<(std::is_integral_v<T> && !std::is_same_v<T, bhalf_t>)
+template <typename Range, typename RefRange>
+std::enable_if_t<(std::is_same_v<ranges::range_value_t<Range>, ranges::range_value_t<RefRange>> &&
+                  std::is_integral_v<ranges::range_value_t<Range>> &&
+                  !std::is_same_v<ranges::range_value_t<Range>, bhalf_t>)
 #ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-                     || std::is_same_v<T, int4_t>
+                     || std::is_same_v<ranges::range_value_t<Range>, int4_t>
 #endif
                  ,
                  bool>
-check_err(const std::vector<T>& out,
-          const std::vector<T>& ref,
+check_err(const Range& out,
+          const RefRange& ref,
           const std::string& msg = "Error: Incorrect results!",
           double                 = 0,
           double atol            = 0)
@@ -188,9 +191,9 @@ check_err(const std::vector<T>& out,
     int64_t max_err = std::numeric_limits<int64_t>::min();
     for(std::size_t i = 0; i < ref.size(); ++i)
     {
-        int64_t o = out[i];
-        int64_t r = ref[i];
-        err       = std::abs(o - r);
+        const int64_t o = *std::next(std::begin(out), i);
+        const int64_t r = *std::next(std::begin(ref), i);
+        err             = std::abs(o - r);
 
         if(err > atol)
         {

library/include/ck/library/utility/host_tensor.hpp

@@ -14,6 +14,9 @@
 #include "ck/utility/data_type.hpp"
 #include "ck/utility/span.hpp"
 
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/ranges.hpp"
+
 template <typename Range>
 std::ostream& LogRange(std::ostream& os, Range&& range, std::string delim)
 {
@@ -84,10 +87,10 @@ struct HostTensorDescriptor
         this->CalculateStrides();
     }
 
-    template <typename Range,
+    template <typename Lengths,
               typename = std::enable_if_t<
-                  std::is_convertible_v<decltype(*std::begin(std::declval<Range>())), std::size_t>>>
-    HostTensorDescriptor(const Range& lens) : mLens(lens.begin(), lens.end())
+                  std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t>>>
+    HostTensorDescriptor(const Lengths& lens) : mLens(lens.begin(), lens.end())
     {
         this->CalculateStrides();
     }
@@ -102,13 +105,12 @@ struct HostTensorDescriptor
     {
     }
 
-    template <
-        typename Range1,
-        typename Range2,
-        typename = std::enable_if_t<
-            std::is_convertible_v<decltype(*std::begin(std::declval<Range1>())), std::size_t> &&
-            std::is_convertible_v<decltype(*std::begin(std::declval<Range2>())), std::size_t>>>
-    HostTensorDescriptor(const Range1& lens, const Range2& strides)
+    template <typename Lengths,
+              typename Strides,
+              typename = std::enable_if_t<
+                  std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> &&
+                  std::is_convertible_v<ck::ranges::range_value_t<Strides>, std::size_t>>>
+    HostTensorDescriptor(const Lengths& lens, const Strides& strides)
         : mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
     {
     }
@@ -244,14 +246,20 @@ struct Tensor
     {
     }
 
-    template <typename X>
-    Tensor(std::vector<X> lens) : mDesc(lens), mData(mDesc.GetElementSpaceSize())
+    template <typename X, typename Y>
+    Tensor(std::initializer_list<X> lens, std::initializer_list<Y> strides)
+        : mDesc(lens, strides), mData(mDesc.GetElementSpaceSize())
     {
     }
 
-    template <typename X, typename Y>
-    Tensor(std::vector<X> lens, std::vector<Y> strides)
-        : mDesc(lens, strides), mData(mDesc.GetElementSpaceSize())
+    template <typename Lengths>
+    Tensor(const Lengths& lens) : mDesc(lens), mData(mDesc.GetElementSpaceSize())
+    {
+    }
+
+    template <typename Lengths, typename Strides>
+    Tensor(const Lengths& lens, const Strides& strides)
+        : mDesc(lens, strides), mData(GetElementSpaceSize())
     {
     }
 
@@ -261,10 +269,10 @@ struct Tensor
     Tensor<OutT> CopyAsType() const
     {
         Tensor<OutT> ret(mDesc);
-        for(size_t i = 0; i < mData.size(); i++)
-        {
-            ret.mData[i] = ck::type_convert<OutT>(mData[i]);
-        }
+
+        ck::ranges::transform(
+            mData, ret.mData.begin(), [](auto value) { return ck::type_convert<OutT>(value); });
+
         return ret;
     }
 
@@ -294,13 +302,7 @@ struct Tensor
 
     std::size_t GetElementSpaceSizeInBytes() const { return sizeof(T) * GetElementSpaceSize(); }
 
-    void SetZero()
-    {
-        for(auto& v : mData)
-        {
-            v = T{0};
-        }
-    }
+    void SetZero() { ck::ranges::fill<T>(mData, 0); }
 
     template <typename F>
     void ForEach_impl(F&& f, std::vector<size_t>& idx, size_t rank)

library/include/ck/library/utility/iterator.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iterator>
+#include <utility>
+
+#include "ck/utility/type.hpp"
+
+namespace ck {
+
+template <typename T>
+using iter_value_t = typename std::iterator_traits<remove_cvref_t<T>>::value_type;
+
+template <typename T>
+using iter_reference_t = decltype(*std::declval<T&>());
+
+template <typename T>
+using iter_difference_t = typename std::iterator_traits<remove_cvref_t<T>>::difference_type;
+
+} // namespace ck

library/include/ck/library/utility/ranges.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iterator>
+#include <type_traits>
+#include <utility>
+
+#include "ck/library/utility/iterator.hpp"
+
+namespace ck {
+namespace ranges {
+
+template <typename R>
+using iterator_t = decltype(std::begin(std::declval<R&>()));
+
+template <typename R>
+using sentinel_t = decltype(std::end(std::declval<R&>()));
+
+template <typename R>
+using range_size_t = decltype(std::size(std::declval<R&>()));
+
+template <typename R>
+using range_difference_t = ck::iter_difference_t<ranges::iterator_t<R>>;
+
+template <typename R>
+using range_value_t = iter_value_t<ranges::iterator_t<R>>;
+
+template <typename R>
+using range_reference_t = iter_reference_t<ranges::iterator_t<R>>;
+
+template <typename T, typename = void>
+struct is_range : std::false_type
+{
+};
+
+template <typename T>
+struct is_range<
+    T,
+    std::void_t<decltype(std::begin(std::declval<T&>())), decltype(std::end(std::declval<T&>()))>>
+    : std::true_type
+{
+};
+
+template <typename T>
+inline constexpr bool is_range_v = is_range<T>::value;
+
+template <typename T, typename = void>
+struct is_sized_range : std::false_type
+{
+};
+
+template <typename T>
+struct is_sized_range<T, std::void_t<decltype(std::size(std::declval<T&>()))>>
+    : std::bool_constant<is_range_v<T>>
+{
+};
+} // namespace ranges
+} // namespace ck

profiler/include/profile_batched_gemm_add_relu_gemm_add_impl.hpp

@@ -14,6 +14,7 @@
 #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"
 
 namespace ck {
@@ -111,15 +112,15 @@ bool profile_batched_gemm_add_relu_gemm_add_impl(bool do_verification,
                                        std::size_t stride,
                                        std::size_t batch_stride,
                                        auto layout) {
+        using namespace ck::literals;
+
         if(std::is_same<decltype(layout), Row>::value)
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
-                                        std::vector<std::size_t>({batch_stride, stride, 1}));
+            return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
         }
         else
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
-                                        std::vector<std::size_t>({batch_stride, 1, stride}));
+            return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
         }
     };
 
@@ -330,8 +331,7 @@ bool profile_batched_gemm_add_relu_gemm_add_impl(bool do_verification,
             {
                 e1_g_m_o_device_buf.FromDevice(e1_g_m_o_device_result.mData.data());
 
-                pass = pass & ck::utils::check_err(e1_g_m_o_device_result.mData,
-                                                   e1_g_m_o_host_result.mData);
+                pass = pass & ck::utils::check_err(e1_g_m_o_device_result, e1_g_m_o_host_result);
 
                 if(do_log)
                 {

profiler/include/profile_batched_gemm_gemm_impl.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"
 #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
 
 namespace ck {
@@ -105,15 +106,15 @@ bool profile_batched_gemm_gemm_impl(bool do_verification,
                                        std::size_t stride,
                                        std::size_t batch_stride,
                                        auto layout) {
+        using namespace ck::literals;
+
         if(std::is_same<decltype(layout), Row>::value)
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
-                                        std::vector<std::size_t>({batch_stride, stride, 1}));
+            return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
         }
         else
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
-                                        std::vector<std::size_t>({batch_stride, 1, stride}));
+            return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
         }
     };
 
@@ -283,8 +284,7 @@ bool profile_batched_gemm_gemm_impl(bool do_verification,
             {
                 c_g_m_o_device_buf.FromDevice(c_g_m_o_device_result.mData.data());
 
-                pass = pass &
-                       ck::utils::check_err(c_g_m_o_device_result.mData, c_g_m_o_host_result.mData);
+                pass = pass & ck::utils::check_err(c_g_m_o_device_result, c_g_m_o_host_result);
 
                 if(do_log)
                 {

profiler/include/profile_batched_gemm_impl.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"
 #include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
 
 namespace ck {
@@ -50,15 +51,15 @@ bool profile_batched_gemm_impl(int do_verification,
                                        std::size_t stride,
                                        std::size_t batch_stride,
                                        auto layout) {
+        using namespace ck::literals;
+
         if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
-                                        std::vector<std::size_t>({batch_stride, stride, 1}));
+            return HostTensorDescriptor({batch_count, row, col}, {batch_stride, stride, 1_uz});
         }
         else
         {
-            return HostTensorDescriptor(std::vector<std::size_t>({batch_count, row, col}),
-                                        std::vector<std::size_t>({batch_stride, 1, stride}));
+            return HostTensorDescriptor({batch_count, row, col}, {batch_stride, 1_uz, stride});
         }
     };
 
@@ -202,8 +203,7 @@ bool profile_batched_gemm_impl(int do_verification,
             {
                 c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
 
-                pass = pass &
-                       ck::utils::check_err(c_g_m_n_device_result.mData, c_g_m_n_host_result.mData);
+                pass = pass & ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
 
                 if(do_log)
                 {

profiler/include/profile_batched_gemm_reduce_impl.hpp

@@ -14,6 +14,7 @@
 #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"
 
 namespace ck {
@@ -78,15 +79,15 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
                                        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});
         }
     };
 
@@ -95,17 +96,13 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
 
     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;
@@ -319,12 +316,9 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
                 reduce0_device_buf.FromDevice(d0_g_m_device_result.mData.data());
                 reduce1_device_buf.FromDevice(d1_g_m_device_result.mData.data());
 
-                bool c_error =
-                    ck::utils::check_err(c_g_m_n_device_result.mData, c_g_m_n_host_result.mData);
-                bool d0_error =
-                    ck::utils::check_err(d0_g_m_device_result.mData, d0_g_m_host_result.mData);
-                bool d1_error =
-                    ck::utils::check_err(d1_g_m_device_result.mData, d1_g_m_host_result.mData);
+                bool c_error  = ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
+                bool d0_error = ck::utils::check_err(d0_g_m_device_result, d0_g_m_host_result);
+                bool d1_error = ck::utils::check_err(d1_g_m_device_result, d1_g_m_host_result);
 
                 pass = pass && (c_error == true);
                 pass = pass && (d0_error == true);