merge dev branch

test/convnd_fwd/conv_util.hpp

@@ -10,7 +10,8 @@
 #include "host_tensor.hpp"
 #include "sequence.hpp"
 
-namespace {
+namespace test {
+namespace conv {
 
 template <ck::index_t... Is>
 using S = ck::Sequence<Is...>;
@@ -19,6 +20,9 @@ using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
 using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
 using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
 
+using DeviceConvFwdNoOpPtr =
+    ck::tensor_operation::device::DeviceConvFwdPtr<InElementOp, WeiElementOp, OutElementOp>;
+
 static constexpr auto ConvFwdDefault =
     ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
 
@@ -62,26 +66,14 @@ using DeviceConvNDFwdInstance = ck::tensor_operation::device::
         1>;                 // CThreadTransferDstScalarPerVector
 // clang-format on
 
-} // namespace
-
-namespace test {
-namespace conv {
-
 template <ck::index_t NDim,
           typename InDataType  = float,
           typename WeiDataType = float,
           typename OutDataType = float>
-void RunConv(const ck::utils::conv::ConvParams& params,
-             const Tensor<InDataType>& input,
-             const Tensor<WeiDataType>& weights,
-             Tensor<OutDataType>& output)
+void get_test_convolution_fwd_instance(std::vector<DeviceConvFwdNoOpPtr>& instances)
 {
-    ck::utils::conv::run_convolution_forward<NDim,
-                                             InDataType,
-                                             WeiDataType,
-                                             OutDataType,
-                                             DeviceConvNDFwdInstance>(
-        params, input, weights, output);
+    using ConvInstanceT = DeviceConvNDFwdInstance<NDim, InDataType, WeiDataType, OutDataType>;
+    instances.emplace_back(std::make_unique<ConvInstanceT>());
 }
 
 } // namespace conv

test/reduce/reduce_no_index.cpp

@@ -37,19 +37,6 @@ static inline std::vector<int> get_invariant_dims(const std::vector<int>& reduce
     return invariantDims;
 };
 
-// map the data type used by the GPU kernels to the corresponding type used by the host codes
-template <typename InType>
-struct type_mapping
-{
-    using OutType = InType;
-};
-
-template <>
-struct type_mapping<ck::half_t>
-{
-    using OutType = half_float::half;
-};
-
 constexpr int Rank = 4;
 
 constexpr ReduceTensorOp ReduceOpId      = ReduceTensorOp::AVG;
@@ -226,13 +213,9 @@ bool test_reduce_no_index_impl(int init_method,
 
     bool result = true;
 
-    using HostInDataType  = typename type_mapping<InDataType>::OutType;
-    using HostOutDataType = typename type_mapping<OutDataType>::OutType;
-    using HostAccDataType = typename type_mapping<AccDataType>::OutType;
-
-    ReductionHost<HostInDataType,
-                  HostAccDataType,
-                  HostOutDataType,
+    ReductionHost<InDataType,
+                  AccDataType,
+                  OutDataType,
                   ReduceOpId,
                   Rank,
                   NumReduceDim,
@@ -240,11 +223,7 @@ bool test_reduce_no_index_impl(int init_method,
                   NeedIndices>
         hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
 
-    hostReduce.Run(alpha,
-                   reinterpret_cast<const HostInDataType*>(in.mData.data()),
-                   beta,
-                   reinterpret_cast<HostOutDataType*>(out_ref.mData.data()),
-                   nullptr);
+    hostReduce.Run(alpha, in.mData.data(), beta, out_ref.mData.data(), nullptr);
 
     const auto i_inLengths  = to_int_vector(inLengths);
     const auto i_inStrides  = to_int_vector(inStrides);

test/reduce/reduce_with_index.cpp

@@ -36,19 +36,6 @@ static inline std::vector<int> get_invariant_dims(const std::vector<int>& reduce
     return invariantDims;
 };
 
-// map the data type used by the GPU kernels to the corresponding type used by the host codes
-template <typename InType>
-struct type_mapping
-{
-    using OutType = InType;
-};
-
-template <>
-struct type_mapping<ck::half_t>
-{
-    using OutType = half_float::half;
-};
-
 constexpr int Rank = 4;
 
 constexpr ReduceTensorOp ReduceOpId      = ReduceTensorOp::AMAX;
@@ -209,13 +196,9 @@ bool test_reduce_with_index_impl(int init_method,
 
     bool result = true;
 
-    using HostInDataType  = typename type_mapping<InDataType>::OutType;
-    using HostOutDataType = typename type_mapping<OutDataType>::OutType;
-    using HostAccDataType = typename type_mapping<AccDataType>::OutType;
-
-    ReductionHost<HostInDataType,
-                  HostAccDataType,
-                  HostOutDataType,
+    ReductionHost<InDataType,
+                  AccDataType,
+                  OutDataType,
                   ReduceOpId,
                   Rank,
                   NumReduceDim,
@@ -223,11 +206,8 @@ bool test_reduce_with_index_impl(int init_method,
                   NeedIndices>
         hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
 
-    hostReduce.Run(alpha,
-                   reinterpret_cast<const HostInDataType*>(in.mData.data()),
-                   beta,
-                   reinterpret_cast<HostOutDataType*>(out_ref.mData.data()),
-                   out_indices_ref.mData.data());
+    hostReduce.Run(
+        alpha, in.mData.data(), beta, out_ref.mData.data(), out_indices_ref.mData.data());
 
     const auto i_inLengths  = to_int_vector(inLengths);
     const auto i_inStrides  = to_int_vector(inStrides);

test/reference_conv_fwd/CMakeLists.txt

 add_test_executable(test_reference_conv_fwd reference_conv_fwd.cpp)
-target_link_libraries(test_reference_conv_fwd PRIVATE host_tensor)
+target_link_libraries(test_reference_conv_fwd PRIVATE host_tensor conv_fwd_util)

test/reference_conv_fwd/reference_conv_fwd.cpp

-#include <algorithm>
 #include <cmath>
 #include <cstdlib>
 #include <half.hpp>
@@ -10,6 +9,7 @@
 #include "config.hpp"
 #include "conv_fwd_util.hpp"
 #include "element_wise_operation.hpp"
+#include "fill.hpp"
 #include "host_tensor.hpp"
 #include "reference_conv_fwd.hpp"
 #include "tensor_layout.hpp"
@@ -19,35 +19,6 @@ using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
 using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
 using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
 
-template <typename T>
-struct FillMonotonicSeq
-{
-    T m_init_value{0};
-    T m_step{1};
-
-    template <typename ForwardIter>
-    void operator()(ForwardIter first, ForwardIter last) const
-    {
-        std::generate(first, last, [=, n = m_init_value]() mutable {
-            auto tmp = n;
-            n += m_step;
-            return tmp;
-        });
-    }
-};
-
-template <typename T>
-struct FillConstant
-{
-    T m_value{0};
-
-    template <typename ForwardIter>
-    void operator()(ForwardIter first, ForwardIter last) const
-    {
-        std::fill(first, last, m_value);
-    }
-};
-
 template <ck::index_t NDim,
           typename InDataType    = float,
           typename WeiDataType   = float,
@@ -55,8 +26,8 @@ template <ck::index_t NDim,
           typename InLayout      = ck::tensor_layout::convolution::NHWC,
           typename WeiLayout     = ck::tensor_layout::convolution::KYXC,
           typename OutLayout     = ck::tensor_layout::convolution::NHWK,
-          typename FillInputOp   = FillMonotonicSeq<InDataType>,
-          typename FillWeightsOp = FillConstant<WeiDataType>>
+          typename FillInputOp   = ck::utils::FillMonotonicSeq<InDataType>,
+          typename FillWeightsOp = ck::utils::FillConstant<WeiDataType>>
 Tensor<OutDataType>
 run_reference_convolution_forward(const ck::utils::conv::ConvParams& params,
                                   const FillInputOp& fill_input_op     = FillInputOp{},
@@ -251,7 +222,7 @@ bool test_conv1d_nwc()
                                                          ck::tensor_layout::convolution::NWC,
                                                          ck::tensor_layout::convolution::KXC,
                                                          ck::tensor_layout::convolution::NWK>(
-        params, FillMonotonicSeq<float>{0.f, 0.1f});
+        params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
 
     ref_dims = std::vector<std::size_t>{2, 16, 16};
     ref_data = std::vector<float>{
@@ -349,7 +320,7 @@ bool test_conv3d_ncdhw()
                                                         ck::tensor_layout::convolution::NCDHW,
                                                         ck::tensor_layout::convolution::KCZYX,
                                                         ck::tensor_layout::convolution::NKDHW>(
-        params, FillMonotonicSeq<float>{0.f, 0.1f});
+        params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
     std::vector<std::size_t> ref_dims{1, 1, 4, 4, 4};
     std::vector<float> ref_data{
         407.7,     410.40002, 413.09998, 415.80002, 423.90002, 426.6,     429.30002, 432.,
@@ -383,7 +354,7 @@ bool test_conv3d_ncdhw()
                                                    ck::tensor_layout::convolution::NCDHW,
                                                    ck::tensor_layout::convolution::KCZYX,
                                                    ck::tensor_layout::convolution::NKDHW>(
-        params, FillMonotonicSeq<float>{0.f, 0.1f});
+        params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
     ref_dims = std::vector<std::size_t>{1, 2, 4, 4, 4};
     ref_data = std::vector<float>{
         2756.7002, 2764.7998, 2772.9001, 2781.,     2853.9001, 2862.,     2870.1,    2878.2002,