Merge remote-tracking branch 'origin/build_0705' into implicit_gemm_v4r2

composable_kernel/include/kernel_algorithm/gridwise_convolution_implicit_gemm_v3_nchw_cyxk_nkhw_lds_double_buffer.hpp

@@ -191,7 +191,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
 
         // c_thread_mtx definition: this is a mess
         // TODO:: more elegent way of defining c_thread_mtx
-        constexpr auto c_k0k2_n1n2_thread_mtx_desc = make_ConstantMatrixDescriptor(
+        constexpr auto c_k0k2_n1n2_thread_mtx_desc = make_ConstantMatrixDescriptor_packed(
             Number<GemmMRepeat * GemmMPerThreadSubC>{}, Number<N1 * N2>{});
 
         const auto blockwise_gemm = BlockwiseGemmBlockABlockBThreadCTransANormalBNormalC_v2<
@@ -209,15 +209,6 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
             GemmDataPerReadA,
             GemmDataPerReadB>{};
 
-        // choose GEMM implementation here
-        const auto run_blockwise_gemm = [&](auto... Xs) {
-#if 1
-            return blockwise_gemm.Run(Xs...);
-#else
-            return blockwise_gemm.Run_amd_asm(Xs...);
-#endif
-        };
-
         // LDS allocation for input and weight: be careful of alignment
         constexpr index_t max_align = math::lcm(InBlockCopyDstDataPerWrite_N2,
                                                 WeiBlockCopyDataPerAccess_K,
@@ -225,9 +216,10 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
                                                 GemmDataPerReadB);
 
         constexpr index_t in_block_space =
-            in_c_n1_b_n2_block_mem_desc.GetElementSpace(Number<max_align>{});
+            math::integer_least_multiple(in_c_n1_b_n2_block_mem_desc.GetElementSpace(), max_align);
 
-        constexpr index_t wei_block_space = wei_c_k_block_desc.GetElementSpace(Number<max_align>{});
+        constexpr index_t wei_block_space =
+            math::integer_least_multiple(wei_c_k_block_desc.GetElementSpace(), max_align);
 
         __shared__ Float p_in_block_double[2 * in_block_space];
         __shared__ Float p_wei_block_double[2 * wei_block_space];
@@ -291,7 +283,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
                                                                     p_wei_register_clipboard);
 
                         // LDS double buffer: GEMM on current data
-                        run_blockwise_gemm(p_wei_block_now, p_in_block_now, p_out_thread);
+                        blockwise_gemm.Run(p_wei_block_now, p_in_block_now, p_out_thread);
 
                         // LDS double buffer: store next data to LDS
                         blockwise_in_copy.RunStoreRegisterClipboard(p_in_register_clipboard,
@@ -319,7 +311,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
                                                                 p_wei_register_clipboard);
 
                     // LDS double buffer: GEMM on current data
-                    run_blockwise_gemm(p_wei_block_double, p_in_block_double, p_out_thread);
+                    blockwise_gemm.Run(p_wei_block_double, p_in_block_double, p_out_thread);
 
                     // LDS double buffer: store next data to LDS
                     blockwise_in_copy.RunStoreRegisterClipboard(p_in_register_clipboard,
@@ -331,7 +323,7 @@ struct GridwiseConvolutionImplicitGemm_v3_nchw_cyxk_nkhw_lds_double_buffer
                     __syncthreads();
 
                     // LDS double buffer: GEMM on current data
-                    run_blockwise_gemm(p_wei_block_double + wei_block_space,
+                    blockwise_gemm.Run(p_wei_block_double + wei_block_space,
                                        p_in_block_double + in_block_space,
                                        p_out_thread);
                 }

composable_kernel/include/tensor_description/ConstantMatrixDescriptor.hpp

@@ -53,8 +53,8 @@ __host__ __device__ constexpr auto
 }
 
 template <index_t NRow, index_t NCol, index_t RowStride>
-__host__ __device__ constexpr auto
-    make_ConstantMatrixDescriptor_from_ConstantTensorDescriptor(ConstantTensorDescriptor<Sequence<NRow, NCol>, Sequence<RowStride, 1>>
+__host__ __device__ constexpr auto make_ConstantMatrixDescriptor_from_ConstantTensorDescriptor(
+    ConstantTensorDescriptor<Sequence<NRow, NCol>, Sequence<RowStride, 1>>)
 {
     return ConstantMatrixDescriptor<NRow, NCol, RowStride>{};
 }

composable_kernel/include/tensor_description/ConstantMergedTensorDescriptor.hpp

@@ -177,8 +177,9 @@ struct ConstantMergedTensorDescriptor
 
     __host__ __device__ static constexpr auto Pack()
     {
-        using Strides = decltype(calculate_tensor_strides_packed(GetLengths()));
-        return ConstantTensorDescriptor<Lengths, Strides>{};
+        constexpr auto lengths = GetLengths();
+        constexpr auto strides = calculate_tensor_strides_packed(lengths);
+        return ConstantTensorDescriptor<decltype(lengths), decltype(strides)>{};
     }
 };
 

composable_kernel/include/tensor_description/ConstantTensorDescriptor.hpp

@@ -374,7 +374,7 @@ struct ConstantTensorDescriptor
     template <index_t IDim, index_t... FoldIntervals>
     __host__ __device__ static constexpr auto Fold(Number<IDim>, Sequence<FoldIntervals...>)
     {
-        return Fold(Number<IDim>{}, Number<FoldInterVals>{}...);
+        return Fold(Number<IDim>{}, Number<FoldIntervals>{}...);
     }
 
     // this function unfold dimension [FirstUnfoldDim, ..., LastUnfoldDim] into 1 dimension
@@ -417,8 +417,8 @@ struct ConstantTensorDescriptor
 
     __host__ __device__ static constexpr auto Pack()
     {
-        using Strides = decltype(calculate_tensor_strides_packed(Lengths{}));
-        return ConstantTensorDescriptor<Lengths, Strides>{};
+        using packed_strides = decltype(calculate_tensor_strides_packed(Lengths{}));
+        return ConstantTensorDescriptor<Lengths, packed_strides>{};
     }
 
     template <class MapNew2Old>

composable_kernel/include/tensor_operation/blockwise_gemm.hpp

@@ -205,10 +205,10 @@ struct BlockwiseGemmBlockABlockBThreadCTransANormalBNormalC_v2
 
         // thread A, B for GEMM
         constexpr auto a_thread_mtx =
-            make_ConstantMatrixDescriptor(Number<KPerThreadLoop>{}, Number<MPerThread>{});
+            make_ConstantMatrixDescriptor_packed(Number<KPerThreadLoop>{}, Number<MPerThread>{});
 
         constexpr auto b_thread_mtx =
-            make_ConstantMatrixDescriptor(Number<KPerThreadLoop>{}, Number<NPerThread>{});
+            make_ConstantMatrixDescriptor_packed(Number<KPerThreadLoop>{}, Number<NPerThread>{});
 
         // thread A-sub, B-sub for copy
         constexpr auto a_thread_sub_mtx = make_ConstantMatrixDescriptor(

composable_kernel/include/utility/config_amd.hpp.in

@@ -18,7 +18,8 @@ typedef float float4_t __attribute__((ext_vector_type(4)));
 
 using index_t = uint32_t;
 
-__device__ void fused_multiply_accumulate(float& d, const float& s0, const float& s1)
+template <class T>
+__device__ void fused_multiply_accumulate(T& d, const T& s0, const T& s1)
 {
     d += s0 * s1;
 }

composable_kernel/include/utility/config_nvidia.hpp.in

@@ -22,7 +22,8 @@ using float4_t = float4;
 
 using index_t = uint32_t;
 
-__device__ void fused_multiply_accumulate(float& d, const float& s0, const float& s1)
+template <class T>
+__device__ void fused_multiply_accumulate(T& d, const T& s0, const T& s1)
 {
     d += s0 * s1;
 }

composable_kernel/include/utility/integral_constant.hpp

@@ -59,18 +59,5 @@ __host__ __device__ constexpr auto operator%(Number<X>, Number<Y>)
     return Number<X % Y>{};
 }
 
-#if 0
-static constexpr Number<0> 0_c;
-static constexpr Number<1> 1_c;
-static constexpr Number<2> 2_c;
-static constexpr Number<3> 3_c;
-static constexpr Number<4> 4_c;
-static constexpr Number<5> 5_c;
-static constexpr Number<6> 6_c;
-static constexpr Number<7> 7_c;
-static constexpr Number<8> 8_c;
-static constexpr Number<9> 9_c;
-#endif
-
 } // namespace ck
 #endif

driver/CMakeLists.txt

@@ -3,15 +3,15 @@ set(TENSOR_SOURCE
     src/device.cpp;
 )
 
-add_library(tensor SHARED ${TENSOR_SOURCE})
-target_compile_features(tensor PUBLIC)
-set_target_properties(tensor PROPERTIES POSITION_INDEPENDENT_CODE ON)
+add_library(host SHARED ${TENSOR_SOURCE})
+target_compile_features(host PUBLIC)
+set_target_properties(host PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
 if(DEVICE_BACKEND STREQUAL "NVIDIA")
-    target_link_libraries(tensor nvToolsExt cudart)
+    target_link_libraries(host nvToolsExt cudart)
 endif()
 
-install(TARGETS tensor LIBRARY DESTINATION lib) 
+install(TARGETS host LIBRARY DESTINATION lib) 
 
 
 if(DEVICE_BACKEND STREQUAL "AMD")
@@ -21,4 +21,4 @@ elseif(DEVICE_BACKEND STREQUAL "NVIDIA")
 endif()
 
 add_executable(driver ${DRIVER_SOURCE}) 
-target_link_libraries(driver PRIVATE tensor)
+target_link_libraries(driver PRIVATE host)

driver/include/conv_common.hpp

-#ifndef CK_CONV_COMMON_HPP
-#define CK_CONV_COMMON_HPP
+#ifndef CONV_COMMON_HPP
+#define CONV_COMMON_HPP
 
 #include "ConstantTensorDescriptor.hpp"
 
-using namespace ck;
-
 // this is ugly, only for 4d
 template <class InDesc, class WeiDesc>
 constexpr auto get_convolution_output_default_4d_tensor_descriptor(InDesc, WeiDesc)
 {
+    using namespace ck;
+
     constexpr auto in_desc  = InDesc{};
     constexpr auto wei_desc = WeiDesc{};
 
@@ -45,6 +45,8 @@ template <class InDesc,
 constexpr auto get_convolution_with_padding_output_default_4d_tensor_descriptor(
     InDesc, WeiDesc, ConvStrides, ConvDilations, LowerPads, UpperPads)
 {
+    using namespace ck;
+
     constexpr auto in_desc  = InDesc{};
     constexpr auto wei_desc = WeiDesc{};
 
@@ -84,6 +86,8 @@ constexpr auto get_convolution_with_padding_output_default_4d_tensor_descriptor(
 template <class InDesc, class WeiDesc, class OutDesc>
 constexpr std::size_t calculate_convolution_flops(InDesc, WeiDesc, OutDesc)
 {
+    using namespace ck;
+
     constexpr auto wei_desc = WeiDesc{};
     constexpr auto out_desc = OutDesc{};
 
@@ -107,6 +111,8 @@ constexpr std::size_t calculate_convolution_flops(InDesc, WeiDesc, OutDesc)
 template <class Float, class InDesc, class WeiDesc, class OutDesc>
 constexpr std::size_t calculate_convolution_memory_size(Float, InDesc, WeiDesc, OutDesc)
 {
+    using namespace ck;
+
     constexpr auto wei_desc = WeiDesc{};
     constexpr auto out_desc = OutDesc{};
 

driver/include/device.hpp

-#ifndef CK_DEVICE_HPP
-#define CK_DEVICE_HPP
+#ifndef DEVICE_HPP
+#define DEVICE_HPP
 
 #include <memory>
 #include "config.hpp"
 
-using namespace ck;
-
 struct DeviceMem
 {
     DeviceMem() = delete;

driver/include/device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp

@@ -6,8 +6,6 @@
 #include "gridwise_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp"
 #include "gridwise_convolution_implicit_gemm_v3_nchw_cyxk_nkhw_lds_double_buffer.hpp"
 
-using namespace ck;
-
 template <class T, class InDesc, class WeiDesc, class OutDesc>
 void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc,
                                                         const Tensor<T>& in_nchw,
@@ -17,6 +15,8 @@ void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc,
                                                         Tensor<T>& out_nkhw,
                                                         index_t nrepeat)
 {
+    using namespace ck;
+
     constexpr auto I0 = Number<0>{};
     constexpr auto I1 = Number<1>{};
     constexpr auto I2 = Number<2>{};
@@ -135,7 +135,6 @@ void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc,
              WeiBlockCopyClusterLengths_C_K,
              WeiBlockCopyDataPerAccess_K>{};
 
-#if 1
         float time = launch_kernel(run_gridwise_convolution_kernel<decltype(gridwise_conv), T>,
                                    dim3(GridSize),
                                    dim3(BlockSize),
@@ -149,7 +148,6 @@ void device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(InDesc,
                (float)calculate_convolution_flops(InDesc{}, WeiDesc{}, OutDesc{}) /
                    (std::size_t(1000) * 1000 * 1000) / time);
         usleep(std::min(time * 1000, float(10000)));
-#endif
     }
 
     out_nkhw_device_buf.FromDevice(out_nkhw.mData.data());

driver/include/device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp

@@ -6,8 +6,6 @@
 #include "gridwise_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp"
 #include "gridwise_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw_lds_double_buffer.hpp"
 
-using namespace ck;
-
 template <class T,
           class InDesc,
           class WeiDesc,
@@ -24,6 +22,8 @@ void device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw(InDesc,
                                                           ConvDilations,
                                                           index_t nrepeat)
 {
+    using namespace ck;
+
     constexpr auto I0 = Number<0>{};
     constexpr auto I1 = Number<1>{};
     constexpr auto I2 = Number<2>{};

driver/include/host_conv.hpp

+#pragma once
+#include "tensor.hpp"
+#include "common_header.hpp"
+#include "ConstantTensorDescriptor.hpp"
+
+// this is ugly, only for 4d
+template <class TConstTensorDesc>
+void ostream_ConstantTensorDescriptor(TConstTensorDesc, std::ostream& os = std::cout)
+{
+    using namespace ck;
+
+    static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
+
+    constexpr auto I0   = Number<0>{};
+    constexpr auto I1   = Number<1>{};
+    constexpr auto I2   = Number<2>{};
+    constexpr auto I3   = Number<3>{};
+    constexpr auto desc = TConstTensorDesc{};
+
+    os << "Lengths: {" << desc.GetLength(I0) << ", " << desc.GetLength(I1) << ", "
+       << desc.GetLength(I2) << ", " << desc.GetLength(I3) << "}, "
+       << "Strides: {" << desc.GetStride(I0) << ", " << desc.GetStride(I1) << ", "
+       << desc.GetStride(I2) << ", " << desc.GetStride(I3) << "}" << std::endl;
+}
+
+// this is ugly, only for 4d
+template <class TConstTensorDesc>
+auto make_TensorDescriptor(TConstTensorDesc)
+{
+    using namespace ck;
+
+    static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
+
+    constexpr auto I0   = Number<0>{};
+    constexpr auto I1   = Number<1>{};
+    constexpr auto I2   = Number<2>{};
+    constexpr auto I3   = Number<3>{};
+    constexpr auto desc = TConstTensorDesc{};
+
+    std::initializer_list<index_t> lengths = {
+        desc.GetLength(I0), desc.GetLength(I1), desc.GetLength(I2), desc.GetLength(I3)};
+    std::initializer_list<index_t> strides = {
+        desc.GetStride(I0), desc.GetStride(I1), desc.GetStride(I2), desc.GetStride(I3)};
+
+    return TensorDescriptor(lengths, strides);
+}
+
+template <class TIn,
+          class TWei,
+          class TOut,
+          class ConvStrides,
+          class ConvDilations,
+          class LowerPads,
+          class UpperPads>
+void host_direct_convolution(const Tensor<TIn>& in_nchw,
+                             const Tensor<TWei>& wei_kcyx,
+                             Tensor<TOut>& out_nkhw,
+                             ConvStrides,
+                             ConvDilations,
+                             LowerPads,
+                             UpperPads)
+{
+    using namespace ck;
+
+    index_t h_pad_low = LowerPads{}.Get(Number<0>{});
+    index_t w_pad_low = LowerPads{}.Get(Number<1>{});
+
+    index_t h_pad_up = UpperPads{}.Get(Number<0>{});
+    index_t w_pad_up = UpperPads{}.Get(Number<1>{});
+
+    auto f = [&](auto n, auto k, auto ho, auto wo) {
+        double v = 0;
+        for(int c = 0; c < wei_kcyx.mDesc.GetLengths()[1]; ++c)
+        {
+            for(int y = 0; y < wei_kcyx.mDesc.GetLengths()[2]; ++y)
+            {
+                int hi = ho * ConvStrides{}[0] + y * ConvDilations{}[0] - h_pad_low;
+                for(int x = 0; x < wei_kcyx.mDesc.GetLengths()[3]; ++x)
+                {
+                    int wi = wo * ConvStrides{}[1] + x * ConvDilations{}[1] - w_pad_low;
+                    if(hi >= 0 && hi < in_nchw.mDesc.GetLengths()[2] && wi >= 0 &&
+                       wi < in_nchw.mDesc.GetLengths()[3])
+                    {
+                        v += double(in_nchw(n, c, hi, wi)) * double(wei_kcyx(k, c, y, x));
+                    }
+                }
+            }
+        }
+        out_nkhw(n, k, ho, wo) = v;
+    };
+
+    auto f_par = make_ParallelTensorFunctor(f,
+                                            out_nkhw.mDesc.GetLengths()[0],
+                                            out_nkhw.mDesc.GetLengths()[1],
+                                            out_nkhw.mDesc.GetLengths()[2],
+                                            out_nkhw.mDesc.GetLengths()[3]);
+
+    f_par(std::thread::hardware_concurrency());
+}
+
+template <class TIn, class TWei, class TOut, class LowerPads, class UpperPads>
+void host_winograd_3x3_convolution(const Tensor<TIn>& in_nchw,
+                                   const Tensor<TWei>& wei_kcyx,
+                                   Tensor<TOut>& out_nkhw,
+                                   LowerPads,
+                                   UpperPads)
+{
+    using namespace ck;
+
+    constexpr std::size_t HoPerTile = 2;
+    constexpr std::size_t WoPerTile = 2;
+
+    std::size_t N  = in_nchw.mDesc.GetLengths()[0];
+    std::size_t C  = in_nchw.mDesc.GetLengths()[1];
+    std::size_t HI = in_nchw.mDesc.GetLengths()[2];
+    std::size_t WI = in_nchw.mDesc.GetLengths()[3];
+
+    std::size_t K = wei_kcyx.mDesc.GetLengths()[0];
+    std::size_t Y = wei_kcyx.mDesc.GetLengths()[2];
+    std::size_t X = wei_kcyx.mDesc.GetLengths()[3];
+
+    std::size_t HO = out_nkhw.mDesc.GetLengths()[2];
+    std::size_t WO = out_nkhw.mDesc.GetLengths()[3];
+
+    index_t h_pad_low = LowerPads{}.Get(Number<0>{});
+    index_t w_pad_low = LowerPads{}.Get(Number<1>{});
+
+    index_t h_pad_up = UpperPads{}.Get(Number<0>{});
+    index_t w_pad_up = UpperPads{}.Get(Number<1>{});
+
+    std::size_t HiPerTile = HoPerTile + Y - 1;
+    std::size_t WiPerTile = WoPerTile + X - 1;
+
+    std::size_t HTile = (HO + HoPerTile - 1) / HoPerTile;
+    std::size_t WTile = (WO + WoPerTile - 1) / WoPerTile;
+
+    Tensor<double> in_hold({N, C, HTile, WTile, HiPerTile, WiPerTile});
+    Tensor<double> in_transform({N, C, HTile, WTile, HiPerTile, WiPerTile});
+    Tensor<double> wei_transform({K, C, HiPerTile, WiPerTile});
+    Tensor<double> out_transform({N, K, HTile, WTile, HiPerTile, HiPerTile});
+    Tensor<double> out_hold({N, K, HTile, WTile, HoPerTile, WoPerTile});
+
+    auto f_in_hold = [&](auto n, auto c, auto htile, auto wtile) {
+        for(int j = 0; j < HiPerTile; ++j)
+        {
+            int hi = HoPerTile * htile + j - h_pad_low;
+            for(int i = 0; i < WiPerTile; ++i)
+            {
+                int wi = WoPerTile * wtile + i - w_pad_low;
+
+                if(hi >= 0 && hi < in_nchw.mDesc.GetLengths()[2] && wi >= 0 &&
+                   wi < in_nchw.mDesc.GetLengths()[3])
+                {
+                    in_hold(n, c, htile, wtile, j, i) = in_nchw(n, c, hi, wi);
+                }
+                else
+                {
+                    in_hold(n, c, htile, wtile, j, i) = TIn(0);
+                }
+            }
+        }
+    };
+
+    auto f_in_transform = [&](auto n, auto c, auto htile, auto wtile) {
+        in_transform(n, c, htile, wtile, 0, 0) =
+            in_hold(n, c, htile, wtile, 0, 0) - in_hold(n, c, htile, wtile, 0, 2) -
+            in_hold(n, c, htile, wtile, 2, 0) + in_hold(n, c, htile, wtile, 2, 2);
+        in_transform(n, c, htile, wtile, 0, 1) =
+            in_hold(n, c, htile, wtile, 0, 1) + in_hold(n, c, htile, wtile, 0, 2) -
+            in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 2);
+        in_transform(n, c, htile, wtile, 0, 2) =
+            -in_hold(n, c, htile, wtile, 0, 1) + in_hold(n, c, htile, wtile, 0, 2) +
+            in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 2);
+        in_transform(n, c, htile, wtile, 0, 3) =
+            in_hold(n, c, htile, wtile, 0, 1) - in_hold(n, c, htile, wtile, 0, 3) -
+            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 3);
+
+        in_transform(n, c, htile, wtile, 1, 0) =
+            in_hold(n, c, htile, wtile, 1, 0) - in_hold(n, c, htile, wtile, 1, 2) +
+            in_hold(n, c, htile, wtile, 2, 0) - in_hold(n, c, htile, wtile, 2, 2);
+        in_transform(n, c, htile, wtile, 1, 1) =
+            in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) +
+            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
+        in_transform(n, c, htile, wtile, 1, 2) =
+            -in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) -
+            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
+        in_transform(n, c, htile, wtile, 1, 3) =
+            in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 3) +
+            in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 3);
+
+        in_transform(n, c, htile, wtile, 2, 0) =
+            -in_hold(n, c, htile, wtile, 1, 0) + in_hold(n, c, htile, wtile, 1, 2) +
+            in_hold(n, c, htile, wtile, 2, 0) - in_hold(n, c, htile, wtile, 2, 2);
+        in_transform(n, c, htile, wtile, 2, 1) =
+            -in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 2) +
+            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
+        in_transform(n, c, htile, wtile, 2, 2) =
+            in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 2) -
+            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
+        in_transform(n, c, htile, wtile, 2, 3) =
+            -in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 3) +
+            in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 3);
+
+        in_transform(n, c, htile, wtile, 3, 0) =
+            in_hold(n, c, htile, wtile, 1, 0) - in_hold(n, c, htile, wtile, 1, 2) -
+            in_hold(n, c, htile, wtile, 3, 0) + in_hold(n, c, htile, wtile, 3, 2);
+        in_transform(n, c, htile, wtile, 3, 1) =
+            in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) -
+            in_hold(n, c, htile, wtile, 3, 1) - in_hold(n, c, htile, wtile, 3, 2);
+        in_transform(n, c, htile, wtile, 3, 2) =
+            -in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) +
+            in_hold(n, c, htile, wtile, 3, 1) - in_hold(n, c, htile, wtile, 3, 2);
+        in_transform(n, c, htile, wtile, 3, 3) =
+            in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 3) -
+            in_hold(n, c, htile, wtile, 3, 1) + in_hold(n, c, htile, wtile, 3, 3);
+    };
+
+    auto f_wei_transform = [&](auto k, auto c) {
+        wei_transform(k, c, 0, 0) = double(wei_kcyx(k, c, 0, 0));
+        wei_transform(k, c, 0, 1) = 0.5 * double(wei_kcyx(k, c, 0, 0)) +
+                                    0.5 * double(wei_kcyx(k, c, 0, 1)) +
+                                    0.5 * double(wei_kcyx(k, c, 0, 2));
+        wei_transform(k, c, 0, 2) = 0.5 * double(wei_kcyx(k, c, 0, 0)) -
+                                    0.5 * double(wei_kcyx(k, c, 0, 1)) +
+                                    0.5 * double(wei_kcyx(k, c, 0, 2));
+        wei_transform(k, c, 0, 3) = double(wei_kcyx(k, c, 0, 2));
+
+        wei_transform(k, c, 1, 0) = 0.5 * double(wei_kcyx(k, c, 0, 0)) +
+                                    0.5 * double(wei_kcyx(k, c, 1, 0)) +
+                                    0.5 * double(wei_kcyx(k, c, 2, 0));
+        wei_transform(k, c, 1, 1) =
+            0.25 * double(wei_kcyx(k, c, 0, 0)) + 0.25 * double(wei_kcyx(k, c, 0, 1)) +
+            0.25 * double(wei_kcyx(k, c, 0, 2)) + 0.25 * double(wei_kcyx(k, c, 1, 0)) +
+            0.25 * double(wei_kcyx(k, c, 1, 1)) + 0.25 * double(wei_kcyx(k, c, 1, 2)) +
+            0.25 * double(wei_kcyx(k, c, 2, 0)) + 0.25 * double(wei_kcyx(k, c, 2, 1)) +
+            0.25 * double(wei_kcyx(k, c, 2, 2));
+        wei_transform(k, c, 1, 2) =
+            0.25 * double(wei_kcyx(k, c, 0, 0)) - 0.25 * double(wei_kcyx(k, c, 0, 1)) +
+            0.25 * double(wei_kcyx(k, c, 0, 2)) + 0.25 * double(wei_kcyx(k, c, 1, 0)) -
+            0.25 * double(wei_kcyx(k, c, 1, 1)) + 0.25 * double(wei_kcyx(k, c, 1, 2)) +
+            0.25 * double(wei_kcyx(k, c, 2, 0)) - 0.25 * double(wei_kcyx(k, c, 2, 1)) +
+            0.25 * double(wei_kcyx(k, c, 2, 2));
+        wei_transform(k, c, 1, 3) = 0.5 * double(wei_kcyx(k, c, 0, 2)) +
+                                    0.5 * double(wei_kcyx(k, c, 1, 2)) +
+                                    0.5 * double(wei_kcyx(k, c, 2, 2));
+
+        wei_transform(k, c, 2, 0) = 0.5 * double(wei_kcyx(k, c, 0, 0)) -
+                                    0.5 * double(wei_kcyx(k, c, 1, 0)) +
+                                    0.5 * double(wei_kcyx(k, c, 2, 0));
+        wei_transform(k, c, 2, 1) =
+            0.25 * double(wei_kcyx(k, c, 0, 0)) + 0.25 * double(wei_kcyx(k, c, 0, 1)) +
+            0.25 * double(wei_kcyx(k, c, 0, 2)) - 0.25 * double(wei_kcyx(k, c, 1, 0)) -
+            0.25 * double(wei_kcyx(k, c, 1, 1)) - 0.25 * double(wei_kcyx(k, c, 1, 2)) +
+            0.25 * double(wei_kcyx(k, c, 2, 0)) + 0.25 * double(wei_kcyx(k, c, 2, 1)) +
+            0.25 * double(wei_kcyx(k, c, 2, 2));
+        wei_transform(k, c, 2, 2) =
+            0.25 * double(wei_kcyx(k, c, 0, 0)) - 0.25 * double(wei_kcyx(k, c, 0, 1)) +
+            0.25 * double(wei_kcyx(k, c, 0, 2)) - 0.25 * double(wei_kcyx(k, c, 1, 0)) +
+            0.25 * double(wei_kcyx(k, c, 1, 1)) - 0.25 * double(wei_kcyx(k, c, 1, 2)) +
+            0.25 * double(wei_kcyx(k, c, 2, 0)) - 0.25 * double(wei_kcyx(k, c, 2, 1)) +
+            0.25 * double(wei_kcyx(k, c, 2, 2));
+        wei_transform(k, c, 2, 3) = 0.5 * double(wei_kcyx(k, c, 0, 2)) -
+                                    0.5 * double(wei_kcyx(k, c, 1, 2)) +
+                                    0.5 * double(wei_kcyx(k, c, 2, 2));
+
+        wei_transform(k, c, 3, 0) = double(wei_kcyx(k, c, 2, 0));
+        wei_transform(k, c, 3, 1) = 0.5 * double(wei_kcyx(k, c, 2, 0)) +
+                                    0.5 * double(wei_kcyx(k, c, 2, 1)) +
+                                    0.5 * double(wei_kcyx(k, c, 2, 2));
+        wei_transform(k, c, 3, 2) = 0.5 * double(wei_kcyx(k, c, 2, 0)) -
+                                    0.5 * double(wei_kcyx(k, c, 2, 1)) +
+                                    0.5 * double(wei_kcyx(k, c, 2, 2));
+        wei_transform(k, c, 3, 3) = double(wei_kcyx(k, c, 2, 2));
+    };
+
+    auto f_out_transform = [&](auto n, auto k, auto htile, auto wtile) {
+        for(int j = 0; j < HiPerTile; ++j)
+        {
+            for(int i = 0; i < WiPerTile; ++i)
+            {
+                double v = 0;
+                for(int c = 0; c < C; ++c)
+                {
+                    v += in_transform(n, c, htile, wtile, j, i) * wei_transform(k, c, j, i);
+                }
+
+                out_transform(n, k, htile, wtile, j, i) = v;
+            }
+        }
+    };
+
+    auto f_out_hold = [&](auto n, auto k, auto htile, auto wtile) {
+        out_hold(n, k, htile, wtile, 0, 0) =
+            out_transform(n, k, htile, wtile, 0, 0) + out_transform(n, k, htile, wtile, 0, 1) +
+            out_transform(n, k, htile, wtile, 0, 2) + out_transform(n, k, htile, wtile, 1, 0) +
+            out_transform(n, k, htile, wtile, 1, 1) + out_transform(n, k, htile, wtile, 1, 2) +
+            out_transform(n, k, htile, wtile, 2, 0) + out_transform(n, k, htile, wtile, 2, 1) +
+            out_transform(n, k, htile, wtile, 2, 2);
+        out_hold(n, k, htile, wtile, 0, 1) =
+            out_transform(n, k, htile, wtile, 0, 1) - out_transform(n, k, htile, wtile, 0, 2) -
+            out_transform(n, k, htile, wtile, 0, 3) + out_transform(n, k, htile, wtile, 1, 1) -
+            out_transform(n, k, htile, wtile, 1, 2) - out_transform(n, k, htile, wtile, 1, 3) +
+            out_transform(n, k, htile, wtile, 2, 1) - out_transform(n, k, htile, wtile, 2, 2) -
+            out_transform(n, k, htile, wtile, 2, 3);
+        out_hold(n, k, htile, wtile, 1, 0) =
+            out_transform(n, k, htile, wtile, 1, 0) + out_transform(n, k, htile, wtile, 1, 1) +
+            out_transform(n, k, htile, wtile, 1, 2) - out_transform(n, k, htile, wtile, 2, 0) -
+            out_transform(n, k, htile, wtile, 2, 1) - out_transform(n, k, htile, wtile, 2, 2) -
+            out_transform(n, k, htile, wtile, 3, 0) - out_transform(n, k, htile, wtile, 3, 1) -
+            out_transform(n, k, htile, wtile, 3, 2);
+        out_hold(n, k, htile, wtile, 1, 1) =
+            out_transform(n, k, htile, wtile, 1, 1) - out_transform(n, k, htile, wtile, 1, 2) -
+            out_transform(n, k, htile, wtile, 1, 3) - out_transform(n, k, htile, wtile, 2, 1) +
+            out_transform(n, k, htile, wtile, 2, 2) + out_transform(n, k, htile, wtile, 2, 3) -
+            out_transform(n, k, htile, wtile, 3, 1) + out_transform(n, k, htile, wtile, 3, 2) +
+            out_transform(n, k, htile, wtile, 3, 3);
+    };
+
+    auto f_out = [&](auto n, auto k, auto htile, auto wtile) {
+        for(int j = 0; j < HoPerTile; ++j)
+        {
+            std::size_t ho = HoPerTile * htile + j;
+            for(int i = 0; i < WoPerTile; ++i)
+            {
+                std::size_t wo = WoPerTile * wtile + i;
+                out_nkhw(n, k, ho, wo) = out_hold(n, k, htile, wtile, j, i);
+            }
+        }
+    };
+
+    std::size_t num_thread = std::thread::hardware_concurrency();
+
+    make_ParallelTensorFunctor(f_in_hold, N, C, HTile, WTile)(num_thread);
+    make_ParallelTensorFunctor(f_in_transform, N, C, HTile, WTile)(num_thread);
+    make_ParallelTensorFunctor(f_wei_transform, K, C)(num_thread);
+    make_ParallelTensorFunctor(f_out_transform, N, K, HTile, WTile)(num_thread);
+    make_ParallelTensorFunctor(f_out_hold, N, K, HTile, WTile)(num_thread);
+    make_ParallelTensorFunctor(f_out, N, K, HTile, WTile)(num_thread);
+}
+
+template <class T>
+void check_error(const Tensor<T>& ref, const Tensor<T>& result)
+{
+    float error     = 0;
+    float max_diff  = -1;
+    float ref_value = 0, result_value = 0;
+    for(int i = 0; i < ref.mData.size(); ++i)
+    {
+        error += std::abs(double(ref.mData[i]) - double(result.mData[i]));
+        float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
+        if(max_diff < diff)
+        {
+            max_diff     = diff;
+            ref_value    = ref.mData[i];
+            result_value = result.mData[i];
+        }
+    }
+
+    std::cout << "error: " << error << std::endl;
+    std::cout << "max_diff: " << max_diff << ", " << ref_value << ", " << result_value << std::endl;
+}

driver/include/tensor.hpp

-#ifndef CK_TENSOR_HPP
-#define CK_TENSOR_HPP
+#ifndef TENSOR_HPP
+#define TENSOR_HPP
 
 #include <thread>
 #include <vector>

driver/src/driver.cpp

@@ -7,15 +7,14 @@
 #include "ConstantTensorDescriptor.hpp"
 #include "device.hpp"
 #include "conv_common.hpp"
+#include "host_conv.hpp"
 #include "device_convolution_direct_v2_nchw_kcyx_nkhw.hpp"
 #include "device_convolution_implicit_gemm_v1_chwn_cyxk_khwn.hpp"
 #include "device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw.hpp"
 #include "device_convolution_implicit_gemm_v2_chwn_cyxk_khwn.hpp"
 #include "device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp"
 #include "device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp"
-#include "device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw.hpp"
-
-using namespace ck;
+//#include "device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw.hpp"
 
 struct GeneratorTensor_1
 {
@@ -66,357 +65,10 @@ struct GeneratorTensor_Checkboard
     }
 };
 
-// this is ugly, only for 4d
-template <class TConstTensorDesc>
-void ostream_ConstantTensorDescriptor(TConstTensorDesc, std::ostream& os = std::cout)
-{
-    static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
-
-    constexpr auto I0   = Number<0>{};
-    constexpr auto I1   = Number<1>{};
-    constexpr auto I2   = Number<2>{};
-    constexpr auto I3   = Number<3>{};
-    constexpr auto desc = TConstTensorDesc{};
-
-    os << "Lengths: {" << desc.GetLength(I0) << ", " << desc.GetLength(I1) << ", "
-       << desc.GetLength(I2) << ", " << desc.GetLength(I3) << "}, "
-       << "Strides: {" << desc.GetStride(I0) << ", " << desc.GetStride(I1) << ", "
-       << desc.GetStride(I2) << ", " << desc.GetStride(I3) << "}" << std::endl;
-}
-
-// this is ugly, only for 4d
-template <class TConstTensorDesc>
-auto make_TensorDescriptor(TConstTensorDesc)
-{
-    static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
-
-    constexpr auto I0   = Number<0>{};
-    constexpr auto I1   = Number<1>{};
-    constexpr auto I2   = Number<2>{};
-    constexpr auto I3   = Number<3>{};
-    constexpr auto desc = TConstTensorDesc{};
-
-    std::initializer_list<index_t> lengths = {
-        desc.GetLength(I0), desc.GetLength(I1), desc.GetLength(I2), desc.GetLength(I3)};
-    std::initializer_list<index_t> strides = {
-        desc.GetStride(I0), desc.GetStride(I1), desc.GetStride(I2), desc.GetStride(I3)};
-
-    return TensorDescriptor(lengths, strides);
-}
-
-template <class TIn,
-          class TWei,
-          class TOut,
-          class ConvStrides,
-          class ConvDilations,
-          class LowerPads,
-          class UpperPads>
-void host_direct_convolution(const Tensor<TIn>& in_nchw,
-                             const Tensor<TWei>& wei_kcyx,
-                             Tensor<TOut>& out_nkhw,
-                             ConvStrides,
-                             ConvDilations,
-                             LowerPads,
-                             UpperPads)
-{
-    index_t h_pad_low = LowerPads{}.Get(Number<0>{});
-    index_t w_pad_low = LowerPads{}.Get(Number<1>{});
-
-    index_t h_pad_up = UpperPads{}.Get(Number<0>{});
-    index_t w_pad_up = UpperPads{}.Get(Number<1>{});
-
-    auto f = [&](auto n, auto k, auto ho, auto wo) {
-        double v = 0;
-        for(int c = 0; c < wei_kcyx.mDesc.GetLengths()[1]; ++c)
-        {
-            for(int y = 0; y < wei_kcyx.mDesc.GetLengths()[2]; ++y)
-            {
-                int hi = ho * ConvStrides{}[0] + y * ConvDilations{}[0] - h_pad_low;
-                for(int x = 0; x < wei_kcyx.mDesc.GetLengths()[3]; ++x)
-                {
-                    int wi = wo * ConvStrides{}[1] + x * ConvDilations{}[1] - w_pad_low;
-                    if(hi >= 0 && hi < in_nchw.mDesc.GetLengths()[2] && wi >= 0 &&
-                       wi < in_nchw.mDesc.GetLengths()[3])
-                    {
-                        v += double(in_nchw(n, c, hi, wi)) * double(wei_kcyx(k, c, y, x));
-                    }
-                }
-            }
-        }
-        out_nkhw(n, k, ho, wo) = v;
-    };
-
-    auto f_par = make_ParallelTensorFunctor(f,
-                                            out_nkhw.mDesc.GetLengths()[0],
-                                            out_nkhw.mDesc.GetLengths()[1],
-                                            out_nkhw.mDesc.GetLengths()[2],
-                                            out_nkhw.mDesc.GetLengths()[3]);
-
-    f_par(std::thread::hardware_concurrency());
-}
-
-template <class TIn, class TWei, class TOut, class LowerPads, class UpperPads>
-void host_winograd_3x3_convolution(const Tensor<TIn>& in_nchw,
-                                   const Tensor<TWei>& wei_kcyx,
-                                   Tensor<TOut>& out_nkhw,
-                                   LowerPads,
-                                   UpperPads)
-{
-    constexpr std::size_t HoPerTile = 2;
-    constexpr std::size_t WoPerTile = 2;
-
-    std::size_t N  = in_nchw.mDesc.GetLengths()[0];
-    std::size_t C  = in_nchw.mDesc.GetLengths()[1];
-    std::size_t HI = in_nchw.mDesc.GetLengths()[2];
-    std::size_t WI = in_nchw.mDesc.GetLengths()[3];
-
-    std::size_t K = wei_kcyx.mDesc.GetLengths()[0];
-    std::size_t Y = wei_kcyx.mDesc.GetLengths()[2];
-    std::size_t X = wei_kcyx.mDesc.GetLengths()[3];
-
-    std::size_t HO = out_nkhw.mDesc.GetLengths()[2];
-    std::size_t WO = out_nkhw.mDesc.GetLengths()[3];
-
-    index_t h_pad_low = LowerPads{}.Get(Number<0>{});
-    index_t w_pad_low = LowerPads{}.Get(Number<1>{});
-
-    index_t h_pad_up = UpperPads{}.Get(Number<0>{});
-    index_t w_pad_up = UpperPads{}.Get(Number<1>{});
-
-    std::size_t HiPerTile = HoPerTile + Y - 1;
-    std::size_t WiPerTile = WoPerTile + X - 1;
-
-    std::size_t HTile = (HO + HoPerTile - 1) / HoPerTile;
-    std::size_t WTile = (WO + WoPerTile - 1) / WoPerTile;
-
-    Tensor<double> in_hold({N, C, HTile, WTile, HiPerTile, WiPerTile});
-    Tensor<double> in_transform({N, C, HTile, WTile, HiPerTile, WiPerTile});
-    Tensor<double> wei_transform({K, C, HiPerTile, WiPerTile});
-    Tensor<double> out_transform({N, K, HTile, WTile, HiPerTile, HiPerTile});
-    Tensor<double> out_hold({N, K, HTile, WTile, HoPerTile, WoPerTile});
-
-    auto f_in_hold = [&](auto n, auto c, auto htile, auto wtile) {
-        for(int j = 0; j < HiPerTile; ++j)
-        {
-            int hi = HoPerTile * htile + j - h_pad_low;
-            for(int i = 0; i < WiPerTile; ++i)
-            {
-                int wi = WoPerTile * wtile + i - w_pad_low;
-
-                if(hi >= 0 && hi < in_nchw.mDesc.GetLengths()[2] && wi >= 0 &&
-                   wi < in_nchw.mDesc.GetLengths()[3])
-                {
-                    in_hold(n, c, htile, wtile, j, i) = in_nchw(n, c, hi, wi);
-                }
-                else
-                {
-                    in_hold(n, c, htile, wtile, j, i) = TIn(0);
-                }
-            }
-        }
-    };
-
-    auto f_in_transform = [&](auto n, auto c, auto htile, auto wtile) {
-        in_transform(n, c, htile, wtile, 0, 0) =
-            in_hold(n, c, htile, wtile, 0, 0) - in_hold(n, c, htile, wtile, 0, 2) -
-            in_hold(n, c, htile, wtile, 2, 0) + in_hold(n, c, htile, wtile, 2, 2);
-        in_transform(n, c, htile, wtile, 0, 1) =
-            in_hold(n, c, htile, wtile, 0, 1) + in_hold(n, c, htile, wtile, 0, 2) -
-            in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 2);
-        in_transform(n, c, htile, wtile, 0, 2) =
-            -in_hold(n, c, htile, wtile, 0, 1) + in_hold(n, c, htile, wtile, 0, 2) +
-            in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 2);
-        in_transform(n, c, htile, wtile, 0, 3) =
-            in_hold(n, c, htile, wtile, 0, 1) - in_hold(n, c, htile, wtile, 0, 3) -
-            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 3);
-
-        in_transform(n, c, htile, wtile, 1, 0) =
-            in_hold(n, c, htile, wtile, 1, 0) - in_hold(n, c, htile, wtile, 1, 2) +
-            in_hold(n, c, htile, wtile, 2, 0) - in_hold(n, c, htile, wtile, 2, 2);
-        in_transform(n, c, htile, wtile, 1, 1) =
-            in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) +
-            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
-        in_transform(n, c, htile, wtile, 1, 2) =
-            -in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) -
-            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
-        in_transform(n, c, htile, wtile, 1, 3) =
-            in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 3) +
-            in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 3);
-
-        in_transform(n, c, htile, wtile, 2, 0) =
-            -in_hold(n, c, htile, wtile, 1, 0) + in_hold(n, c, htile, wtile, 1, 2) +
-            in_hold(n, c, htile, wtile, 2, 0) - in_hold(n, c, htile, wtile, 2, 2);
-        in_transform(n, c, htile, wtile, 2, 1) =
-            -in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 2) +
-            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
-        in_transform(n, c, htile, wtile, 2, 2) =
-            in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 2) -
-            in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
-        in_transform(n, c, htile, wtile, 2, 3) =
-            -in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 3) +
-            in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 3);
-
-        in_transform(n, c, htile, wtile, 3, 0) =
-            in_hold(n, c, htile, wtile, 1, 0) - in_hold(n, c, htile, wtile, 1, 2) -
-            in_hold(n, c, htile, wtile, 3, 0) + in_hold(n, c, htile, wtile, 3, 2);
-        in_transform(n, c, htile, wtile, 3, 1) =
-            in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) -
-            in_hold(n, c, htile, wtile, 3, 1) - in_hold(n, c, htile, wtile, 3, 2);
-        in_transform(n, c, htile, wtile, 3, 2) =
-            -in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) +
-            in_hold(n, c, htile, wtile, 3, 1) - in_hold(n, c, htile, wtile, 3, 2);
-        in_transform(n, c, htile, wtile, 3, 3) =
-            in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 3) -
-            in_hold(n, c, htile, wtile, 3, 1) + in_hold(n, c, htile, wtile, 3, 3);
-    };
-
-    auto f_wei_transform = [&](auto k, auto c) {
-        wei_transform(k, c, 0, 0) = double(wei_kcyx(k, c, 0, 0));
-        wei_transform(k, c, 0, 1) = 0.5 * double(wei_kcyx(k, c, 0, 0)) +
-                                    0.5 * double(wei_kcyx(k, c, 0, 1)) +
-                                    0.5 * double(wei_kcyx(k, c, 0, 2));
-        wei_transform(k, c, 0, 2) = 0.5 * double(wei_kcyx(k, c, 0, 0)) -
-                                    0.5 * double(wei_kcyx(k, c, 0, 1)) +
-                                    0.5 * double(wei_kcyx(k, c, 0, 2));
-        wei_transform(k, c, 0, 3) = double(wei_kcyx(k, c, 0, 2));
-
-        wei_transform(k, c, 1, 0) = 0.5 * double(wei_kcyx(k, c, 0, 0)) +
-                                    0.5 * double(wei_kcyx(k, c, 1, 0)) +
-                                    0.5 * double(wei_kcyx(k, c, 2, 0));
-        wei_transform(k, c, 1, 1) =
-            0.25 * double(wei_kcyx(k, c, 0, 0)) + 0.25 * double(wei_kcyx(k, c, 0, 1)) +
-            0.25 * double(wei_kcyx(k, c, 0, 2)) + 0.25 * double(wei_kcyx(k, c, 1, 0)) +
-            0.25 * double(wei_kcyx(k, c, 1, 1)) + 0.25 * double(wei_kcyx(k, c, 1, 2)) +
-            0.25 * double(wei_kcyx(k, c, 2, 0)) + 0.25 * double(wei_kcyx(k, c, 2, 1)) +
-            0.25 * double(wei_kcyx(k, c, 2, 2));
-        wei_transform(k, c, 1, 2) =
-            0.25 * double(wei_kcyx(k, c, 0, 0)) - 0.25 * double(wei_kcyx(k, c, 0, 1)) +
-            0.25 * double(wei_kcyx(k, c, 0, 2)) + 0.25 * double(wei_kcyx(k, c, 1, 0)) -
-            0.25 * double(wei_kcyx(k, c, 1, 1)) + 0.25 * double(wei_kcyx(k, c, 1, 2)) +
-            0.25 * double(wei_kcyx(k, c, 2, 0)) - 0.25 * double(wei_kcyx(k, c, 2, 1)) +
-            0.25 * double(wei_kcyx(k, c, 2, 2));
-        wei_transform(k, c, 1, 3) = 0.5 * double(wei_kcyx(k, c, 0, 2)) +
-                                    0.5 * double(wei_kcyx(k, c, 1, 2)) +
-                                    0.5 * double(wei_kcyx(k, c, 2, 2));
-
-        wei_transform(k, c, 2, 0) = 0.5 * double(wei_kcyx(k, c, 0, 0)) -
-                                    0.5 * double(wei_kcyx(k, c, 1, 0)) +
-                                    0.5 * double(wei_kcyx(k, c, 2, 0));
-        wei_transform(k, c, 2, 1) =
-            0.25 * double(wei_kcyx(k, c, 0, 0)) + 0.25 * double(wei_kcyx(k, c, 0, 1)) +
-            0.25 * double(wei_kcyx(k, c, 0, 2)) - 0.25 * double(wei_kcyx(k, c, 1, 0)) -
-            0.25 * double(wei_kcyx(k, c, 1, 1)) - 0.25 * double(wei_kcyx(k, c, 1, 2)) +
-            0.25 * double(wei_kcyx(k, c, 2, 0)) + 0.25 * double(wei_kcyx(k, c, 2, 1)) +
-            0.25 * double(wei_kcyx(k, c, 2, 2));
-        wei_transform(k, c, 2, 2) =
-            0.25 * double(wei_kcyx(k, c, 0, 0)) - 0.25 * double(wei_kcyx(k, c, 0, 1)) +
-            0.25 * double(wei_kcyx(k, c, 0, 2)) - 0.25 * double(wei_kcyx(k, c, 1, 0)) +
-            0.25 * double(wei_kcyx(k, c, 1, 1)) - 0.25 * double(wei_kcyx(k, c, 1, 2)) +
-            0.25 * double(wei_kcyx(k, c, 2, 0)) - 0.25 * double(wei_kcyx(k, c, 2, 1)) +
-            0.25 * double(wei_kcyx(k, c, 2, 2));
-        wei_transform(k, c, 2, 3) = 0.5 * double(wei_kcyx(k, c, 0, 2)) -
-                                    0.5 * double(wei_kcyx(k, c, 1, 2)) +
-                                    0.5 * double(wei_kcyx(k, c, 2, 2));
-
-        wei_transform(k, c, 3, 0) = double(wei_kcyx(k, c, 2, 0));
-        wei_transform(k, c, 3, 1) = 0.5 * double(wei_kcyx(k, c, 2, 0)) +
-                                    0.5 * double(wei_kcyx(k, c, 2, 1)) +
-                                    0.5 * double(wei_kcyx(k, c, 2, 2));
-        wei_transform(k, c, 3, 2) = 0.5 * double(wei_kcyx(k, c, 2, 0)) -
-                                    0.5 * double(wei_kcyx(k, c, 2, 1)) +
-                                    0.5 * double(wei_kcyx(k, c, 2, 2));
-        wei_transform(k, c, 3, 3) = double(wei_kcyx(k, c, 2, 2));
-    };
-
-    auto f_out_transform = [&](auto n, auto k, auto htile, auto wtile) {
-        for(int j = 0; j < HiPerTile; ++j)
-        {
-            for(int i = 0; i < WiPerTile; ++i)
-            {
-                double v = 0;
-                for(int c = 0; c < C; ++c)
-                {
-                    v += in_transform(n, c, htile, wtile, j, i) * wei_transform(k, c, j, i);
-                }
-
-                out_transform(n, k, htile, wtile, j, i) = v;
-            }
-        }
-    };
-
-    auto f_out_hold = [&](auto n, auto k, auto htile, auto wtile) {
-        out_hold(n, k, htile, wtile, 0, 0) =
-            out_transform(n, k, htile, wtile, 0, 0) + out_transform(n, k, htile, wtile, 0, 1) +
-            out_transform(n, k, htile, wtile, 0, 2) + out_transform(n, k, htile, wtile, 1, 0) +
-            out_transform(n, k, htile, wtile, 1, 1) + out_transform(n, k, htile, wtile, 1, 2) +
-            out_transform(n, k, htile, wtile, 2, 0) + out_transform(n, k, htile, wtile, 2, 1) +
-            out_transform(n, k, htile, wtile, 2, 2);
-        out_hold(n, k, htile, wtile, 0, 1) =
-            out_transform(n, k, htile, wtile, 0, 1) - out_transform(n, k, htile, wtile, 0, 2) -
-            out_transform(n, k, htile, wtile, 0, 3) + out_transform(n, k, htile, wtile, 1, 1) -
-            out_transform(n, k, htile, wtile, 1, 2) - out_transform(n, k, htile, wtile, 1, 3) +
-            out_transform(n, k, htile, wtile, 2, 1) - out_transform(n, k, htile, wtile, 2, 2) -
-            out_transform(n, k, htile, wtile, 2, 3);
-        out_hold(n, k, htile, wtile, 1, 0) =
-            out_transform(n, k, htile, wtile, 1, 0) + out_transform(n, k, htile, wtile, 1, 1) +
-            out_transform(n, k, htile, wtile, 1, 2) - out_transform(n, k, htile, wtile, 2, 0) -
-            out_transform(n, k, htile, wtile, 2, 1) - out_transform(n, k, htile, wtile, 2, 2) -
-            out_transform(n, k, htile, wtile, 3, 0) - out_transform(n, k, htile, wtile, 3, 1) -
-            out_transform(n, k, htile, wtile, 3, 2);
-        out_hold(n, k, htile, wtile, 1, 1) =
-            out_transform(n, k, htile, wtile, 1, 1) - out_transform(n, k, htile, wtile, 1, 2) -
-            out_transform(n, k, htile, wtile, 1, 3) - out_transform(n, k, htile, wtile, 2, 1) +
-            out_transform(n, k, htile, wtile, 2, 2) + out_transform(n, k, htile, wtile, 2, 3) -
-            out_transform(n, k, htile, wtile, 3, 1) + out_transform(n, k, htile, wtile, 3, 2) +
-            out_transform(n, k, htile, wtile, 3, 3);
-    };
-
-    auto f_out = [&](auto n, auto k, auto htile, auto wtile) {
-        for(int j = 0; j < HoPerTile; ++j)
-        {
-            std::size_t ho = HoPerTile * htile + j;
-            for(int i = 0; i < WoPerTile; ++i)
-            {
-                std::size_t wo = WoPerTile * wtile + i;
-                out_nkhw(n, k, ho, wo) = out_hold(n, k, htile, wtile, j, i);
-            }
-        }
-    };
-
-    std::size_t num_thread = std::thread::hardware_concurrency();
-
-    make_ParallelTensorFunctor(f_in_hold, N, C, HTile, WTile)(num_thread);
-    make_ParallelTensorFunctor(f_in_transform, N, C, HTile, WTile)(num_thread);
-    make_ParallelTensorFunctor(f_wei_transform, K, C)(num_thread);
-    make_ParallelTensorFunctor(f_out_transform, N, K, HTile, WTile)(num_thread);
-    make_ParallelTensorFunctor(f_out_hold, N, K, HTile, WTile)(num_thread);
-    make_ParallelTensorFunctor(f_out, N, K, HTile, WTile)(num_thread);
-}
-
-template <class T>
-void check_error(const Tensor<T>& ref, const Tensor<T>& result)
-{
-    float error     = 0;
-    float max_diff  = -1;
-    float ref_value = 0, result_value = 0;
-    for(int i = 0; i < ref.mData.size(); ++i)
-    {
-        error += std::abs(double(ref.mData[i]) - double(result.mData[i]));
-        float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
-        if(max_diff < diff)
-        {
-            max_diff     = diff;
-            ref_value    = ref.mData[i];
-            result_value = result.mData[i];
-        }
-    }
-
-    std::cout << "error: " << error << std::endl;
-    std::cout << "max_diff: " << max_diff << ", " << ref_value << ", " << result_value << std::endl;
-}
-
 int main(int argc, char* argv[])
 {
+    using namespace ck;
+
 #if 0
     constexpr index_t N  = 8;
     constexpr index_t C  = 16;
@@ -612,7 +264,7 @@ int main(int argc, char* argv[])
 
     constexpr index_t HPad = 0;
     constexpr index_t WPad = 0;
-#elif 0
+#elif 1
     // 1x1 filter, 8x8 image
     // cudnn@V100 77%, ck@V100 76%, ck@P100 79%, ck@VII 51%
     constexpr index_t N  = 128;
@@ -788,7 +440,7 @@ int main(int argc, char* argv[])
 
     constexpr index_t HPad = 0;
     constexpr index_t WPad = 0;
-#elif 1
+#elif 0
     // 1x1 filter, 7x7 image
     // cudnn@V100 49%, ck@V100 50%, ck@P100 61%, ck@VII 52%
     constexpr index_t N  = 128;
@@ -860,32 +512,31 @@ int main(int argc, char* argv[])
 #endif
     }
 
-#if 1
 #if 0
     device_convolution_direct_v2_nchw_kcyx_nkhw
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
 #elif 0
     device_convolution_implicit_gemm_v1_chwn_cyxk_khwn
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
 #elif 0
     device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
 #elif 0
     device_convolution_implicit_gemm_v2_chwn_cyxk_khwn
-#elif 0
-    device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw
-#elif 0
-    device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
 #elif 1
-    device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw
-#endif
-    (in_nchw_desc,
-     in_nchw,
-     wei_kcyx_desc,
-     wei_kcyx,
-     out_nkhw_desc,
-     out_nkhw_device,
-     ConvStrides{},
-     ConvDilations{},
-     nrepeat);
-
+    device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
+#elif 0
+    device_convolution_implicit_gemm_v4_nchw_kcyx_nkhw(in_nchw_desc,
+                                                       in_nchw,
+                                                       wei_kcyx_desc,
+                                                       wei_kcyx,
+                                                       out_nkhw_desc,
+                                                       out_nkhw_device,
+                                                       ConvStrides{},
+                                                       ConvDilations{},
+                                                       nrepeat);
 #elif 0
     device_implicit_gemm_convolution_1_chwn_cyxk_khwn_padded(in_nchw_desc,
                                                              in_nchw,

driver/src/driver.cu

-driver.cpp
\ No newline at end of file

driver/src/driver.cu

+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include "config.hpp"
+#include "ConstantTensorDescriptor.hpp"
+#include "device.hpp"
+#include "conv_common.hpp"
+#include "host_conv.hpp"
+#include "device_convolution_direct_v2_nchw_kcyx_nkhw.hpp"
+#include "device_convolution_implicit_gemm_v1_chwn_cyxk_khwn.hpp"
+#include "device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw.hpp"
+#include "device_convolution_implicit_gemm_v2_chwn_cyxk_khwn.hpp"
+#include "device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp"
+#include "device_convolution_implicit_gemm_v4r1_nchw_kcyx_nkhw.hpp"
+//#include "device_convolution_implicit_gemm_v4r2_nchw_kcyx_nkhw.hpp"
+
+struct GeneratorTensor_1
+{
+    template <class... Is>
+    double operator()(Is... is)
+    {
+        return 1;
+    }
+};
+
+struct GeneratorTensor_2
+{
+    int min_value = 0;
+    int max_value = 1;
+
+    template <class... Is>
+    double operator()(Is...)
+    {
+        return (std::rand() % (max_value - min_value)) + min_value;
+    }
+};
+
+struct GeneratorTensor_3
+{
+    template <class... Is>
+    double operator()(Is... is)
+    {
+        std::array<index_t, sizeof...(Is)> dims = {{static_cast<index_t>(is)...}};
+
+        auto f_acc = [](auto a, auto b) { return 100 * a + b; };
+
+        return std::accumulate(dims.begin(), dims.end(), index_t(0), f_acc);
+    }
+};
+
+struct GeneratorTensor_Checkboard
+{
+    template <class... Ts>
+    double operator()(Ts... Xs) const
+    {
+        std::array<index_t, sizeof...(Ts)> dims = {{Xs...}};
+        return std::accumulate(dims.begin(),
+                               dims.end(),
+                               true,
+                               [](bool init, index_t x) -> int { return init != (x % 2); })
+                   ? 1
+                   : -1;
+    }
+};
+
+int main(int argc, char* argv[])
+{
+    using namespace ck;
+
+#if 0
+    constexpr index_t N  = 8;
+    constexpr index_t C  = 16;
+    constexpr index_t HI = 3;
+    constexpr index_t WI = 18;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 3;
+    constexpr index_t X  = 3;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 3x3, 34x34
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 256;
+    constexpr index_t HI = 34;
+    constexpr index_t WI = 34;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 3;
+    constexpr index_t X  = 3;
+
+    using ConvStrides   = Sequence<2, 2>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 3x3, 56x56
+    constexpr index_t N  = 64;
+    constexpr index_t C  = 64;
+    constexpr index_t HI = 56;
+    constexpr index_t WI = 56;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 3;
+    constexpr index_t X  = 3;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 3x3 filter, 28x28 image
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 256;
+    constexpr index_t HI = 28;
+    constexpr index_t WI = 28;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 3;
+    constexpr index_t X  = 3;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 28x28 image
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 512;
+    constexpr index_t HI = 28;
+    constexpr index_t WI = 28;
+    constexpr index_t K  = 512;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 3x3 filter, 20x84 image, 1x1 padding
+    constexpr index_t N  = 16;
+    constexpr index_t C  = 256;
+    constexpr index_t HI = 20;
+    constexpr index_t WI = 84;
+    constexpr index_t K  = 256;
+    constexpr index_t Y  = 3;
+    constexpr index_t X  = 3;
+
+    constexpr index_t HPad = 1;
+    constexpr index_t WPad = 1;
+#elif 0
+    // 3x3 filter, 112x112 image, 1x1 padding
+    constexpr index_t N  = 16;
+    constexpr index_t C  = 64;
+    constexpr index_t HI = 112;
+    constexpr index_t WI = 112;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 3;
+    constexpr index_t X  = 3;
+
+    constexpr index_t HPad = 1;
+    constexpr index_t WPad = 1;
+#elif 0
+    // 5x5 filter, 20x86 image
+    constexpr index_t N  = 16;
+    constexpr index_t C  = 256;
+    constexpr index_t HI = 20;
+    constexpr index_t WI = 86;
+    constexpr index_t K  = 512;
+    constexpr index_t Y  = 5;
+    constexpr index_t X  = 5;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 5x5 filter, 20x86 image, 1x1 padding
+    constexpr index_t N  = 16;
+    constexpr index_t C  = 256;
+    constexpr index_t HI = 20;
+    constexpr index_t WI = 86;
+    constexpr index_t K  = 512;
+    constexpr index_t Y  = 5;
+    constexpr index_t X  = 5;
+
+    constexpr index_t HPad = 1;
+    constexpr index_t WPad = 1;
+#elif 0
+    // 5x5 filter, 28x28 image, 2x2 padding
+    constexpr index_t N  = 16;
+    constexpr index_t C  = 192;
+    constexpr index_t HI = 28;
+    constexpr index_t WI = 28;
+    constexpr index_t K  = 32;
+    constexpr index_t Y  = 5;
+    constexpr index_t X  = 5;
+
+    constexpr index_t HPad = 2;
+    constexpr index_t WPad = 2;
+#elif 0
+    // 3x3 filter, 14x14 image
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 256;
+    constexpr index_t HI = 14;
+    constexpr index_t WI = 14;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 3;
+    constexpr index_t X  = 3;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 14x14 image
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 512;
+    constexpr index_t HI = 14;
+    constexpr index_t WI = 14;
+    constexpr index_t K  = 512;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 7x7 image
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 512;
+    constexpr index_t HI = 7;
+    constexpr index_t WI = 7;
+    constexpr index_t K  = 2048;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 73x73 image
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 512;
+    constexpr index_t HI = 73;
+    constexpr index_t WI = 73;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 8x8 image
+    // cudnn@V100 68%, ck@V100 72%, ck@P100 52%, ck@VII 42%
+    constexpr index_t N  = 64;
+    constexpr index_t C  = 1536;
+    constexpr index_t HI = 8;
+    constexpr index_t WI = 8;
+    constexpr index_t K  = 256;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 1
+    // 1x1 filter, 8x8 image
+    // cudnn@V100 77%, ck@V100 76%, ck@P100 79%, ck@VII 51%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 2048;
+    constexpr index_t HI = 8;
+    constexpr index_t WI = 8;
+    constexpr index_t K  = 384;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 7x7 image
+    // cudnn@V100 82%, ck@V100 76%, ck@P100 67%, ck@VII 64%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 832;
+    constexpr index_t HI = 7;
+    constexpr index_t WI = 7;
+    constexpr index_t K  = 384;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 8x8 image
+    // cudnn@V100 83%, ck@V100 75%, ck@P100 78%, ck@VII 65%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 1280;
+    constexpr index_t HI = 8;
+    constexpr index_t WI = 8;
+    constexpr index_t K  = 384;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 14x14 image
+    // cudnn@V100 62%, ck@V100 68%, ck@P100 70%, ck@VII 50%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 512;
+    constexpr index_t HI = 14;
+    constexpr index_t WI = 14;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 8x8 image
+    // cudnn@V100 74%, ck@V100 57%, ck@P100 78%, ck@VII 61%
+    constexpr index_t N  = 64;
+    constexpr index_t C  = 1536;
+    constexpr index_t HI = 8;
+    constexpr index_t WI = 8;
+    constexpr index_t K  = 384;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 28x28 image
+    // cudnn@V100 86%, ck@V100 84%, ck@P100 80%, ck@VII 69%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 256;
+    constexpr index_t HI = 28;
+    constexpr index_t WI = 28;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 7x7 image
+    // cudnn@V100 71%, ck@V100 55%, ck@P100 70%, ck@VII 62%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 832;
+    constexpr index_t HI = 7;
+    constexpr index_t WI = 7;
+    constexpr index_t K  = 256;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 3x3 filter, 2x2 stride, 35x35 input, 17x17 output
+    // cudnn@V100 90%, ck@V100 93%, ck@P100 83%, ck@VII 81%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 288;
+    constexpr index_t HI = 35;
+    constexpr index_t WI = 35;
+    constexpr index_t K  = 384;
+    constexpr index_t Y  = 3;
+    constexpr index_t X  = 3;
+
+    using ConvStrides   = Sequence<2, 2>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 17x17 input
+    // cudnn@V100 81%, ck@V100 76%, ck@P100 70%, ck@VII 76%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 768;
+    constexpr index_t HI = 17;
+    constexpr index_t WI = 17;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 14x14 image
+    // cudnn@V100 73%, ck@V100 71%, ck@P100 70%, ck@VII 64%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 528;
+    constexpr index_t HI = 14;
+    constexpr index_t WI = 14;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 14x14 image
+    // cudnn@V100 73%, ck@V100 72%, ck@P100 79%, ck@VII 75%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 528;
+    constexpr index_t HI = 14;
+    constexpr index_t WI = 14;
+    constexpr index_t K  = 256;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#elif 0
+    // 1x1 filter, 7x7 image
+    // cudnn@V100 49%, ck@V100 50%, ck@P100 61%, ck@VII 52%
+    constexpr index_t N  = 128;
+    constexpr index_t C  = 832;
+    constexpr index_t HI = 7;
+    constexpr index_t WI = 7;
+    constexpr index_t K  = 128;
+    constexpr index_t Y  = 1;
+    constexpr index_t X  = 1;
+
+    using ConvStrides   = Sequence<1, 1>;
+    using ConvDilations = Sequence<1, 1>;
+
+    constexpr index_t HPad = 0;
+    constexpr index_t WPad = 0;
+#endif
+
+    auto lower_pads = Sequence<HPad, WPad>{};
+    auto upper_pads = Sequence<HPad, WPad>{};
+
+    auto in_nchw_desc  = make_ConstantTensorDescriptor_packed(Sequence<N, C, HI, WI>{});
+    auto wei_kcyx_desc = make_ConstantTensorDescriptor_packed(Sequence<K, C, Y, X>{});
+    auto out_nkhw_desc = get_convolution_with_padding_output_default_4d_tensor_descriptor(
+        in_nchw_desc, wei_kcyx_desc, ConvStrides{}, ConvDilations{}, lower_pads, upper_pads);
+
+    ostream_ConstantTensorDescriptor(in_nchw_desc, std::cout << "in_nchw_desc: ");
+    ostream_ConstantTensorDescriptor(wei_kcyx_desc, std::cout << "wei_kcyx_desc: ");
+    ostream_ConstantTensorDescriptor(out_nkhw_desc, std::cout << "out_nkhw_desc: ");
+
+    using in_data_t  = float;
+    using out_data_t = float;
+    Tensor<in_data_t> in_nchw(make_TensorDescriptor(in_nchw_desc));
+    Tensor<in_data_t> wei_kcyx(make_TensorDescriptor(wei_kcyx_desc));
+    Tensor<out_data_t> out_nkhw_host(make_TensorDescriptor(out_nkhw_desc));
+    Tensor<out_data_t> out_nkhw_device(make_TensorDescriptor(out_nkhw_desc));
+
+    std::size_t num_thread = std::thread::hardware_concurrency();
+
+    if(argc != 3)
+    {
+        printf("arg1: do_verification, arg2: nrepeat\n");
+        exit(1);
+    }
+
+    bool do_verification = atoi(argv[1]);
+    index_t nrepeat      = atoi(argv[2]);
+
+    if(do_verification)
+    {
+#if 0
+        in_nchw.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        wei_kcyx.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+#elif 0
+        in_nchw.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+        wei_kcyx.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+#elif 0
+        in_nchw.GenerateTensorValue(GeneratorTensor_3{}, num_thread);
+        wei_kcyx.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
+#elif 1
+        in_nchw.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+        wei_kcyx.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
+#elif 0
+        in_nchw.GenerateTensorValue(GeneratorTensor_2{1, 5}, num_thread);
+
+        auto gen_wei = [](auto... is) {
+            return GeneratorTensor_2{1, 5}(is...) * GeneratorTensor_Checkboard{}(is...);
+        };
+        wei_kcyx.GenerateTensorValue(gen_wei, num_thread);
+#endif
+    }
+
+#if 0
+    device_convolution_direct_v2_nchw_kcyx_nkhw
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
+#elif 0
+    device_convolution_implicit_gemm_v1_chwn_cyxk_khwn
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
+#elif 0
+    device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
+#elif 0
+    device_convolution_implicit_gemm_v2_chwn_cyxk_khwn
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
+#elif 1
+    device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw(
+        in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
+#elif 0
+    device_convolution_implicit_gemm_v4_nchw_kcyx_nkhw(in_nchw_desc,
+                                                       in_nchw,
+                                                       wei_kcyx_desc,
+                                                       wei_kcyx,
+                                                       out_nkhw_desc,
+                                                       out_nkhw_device,
+                                                       ConvStrides{},
+                                                       ConvDilations{},
+                                                       nrepeat);
+#elif 0
+    device_implicit_gemm_convolution_1_chwn_cyxk_khwn_padded(in_nchw_desc,
+                                                             in_nchw,
+                                                             wei_kcyx_desc,
+                                                             wei_kcyx,
+                                                             out_nkhw_desc,
+                                                             out_nkhw_device,
+                                                             lower_pads,
+                                                             upper_pads,
+                                                             nrepeat);
+#endif
+
+    if(do_verification)
+    {
+#if 1
+        if(Y == 3 && X == 3 && ConvStrides{}[0] == 1 && ConvStrides{}[1] == 1 &&
+           ConvDilations{}[0] == 1 && ConvDilations{}[1] == 1)
+        {
+            host_winograd_3x3_convolution(in_nchw, wei_kcyx, out_nkhw_host, lower_pads, upper_pads);
+        }
+        else
+#endif
+        {
+            host_direct_convolution(in_nchw,
+                                    wei_kcyx,
+                                    out_nkhw_host,
+                                    ConvStrides{},
+                                    ConvDilations{},
+                                    lower_pads,
+                                    upper_pads);
+        }
+        check_error(out_nkhw_host, out_nkhw_device);
+
+#if 0
+        LogRange(std::cout << "in_nchw : ", in_nchw.mData, ",") << std::endl;
+        LogRange(std::cout << "wei_kcyx: ", wei_kcyx.mData, ",") << std::endl;
+        LogRange(std::cout << "out_nkhw_host  : ", out_nkhw_host.mData, ",") << std::endl;
+        LogRange(std::cout << "out_nkhw_device: ", out_nkhw_device.mData, ",") << std::endl;
+#endif
+    }
+}