[CK_TILE] Image to Column kernel (#1532)

* [CK_TILE] Image to Column kernel

* Fixes

* Vector loads and stores

* Fixes

* Fixes

* change test dir name

include/ck_tile/host/convolution_host_tensor_descriptor_helper.hpp

@@ -0,0 +1,266 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/host/convolution_parameter.hpp"
+#include "ck_tile/host/host_tensor.hpp"
+
+namespace ck_tile {
+namespace conv {
+namespace detail {
+
+template <typename OldLayout>
+CK_TILE_HOST std::vector<std::size_t> get_layout_transpose_gnchw_to_old()
+{
+    if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNCW> ||
+                 std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKCX> ||
+                 std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNKW>)
+    {
+        return {0, 1, 2, 3};
+    }
+    else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNCHW> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKCYX> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNKHW>)
+    {
+        return {0, 1, 2, 3, 4};
+    }
+    else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNCDHW> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKCZYX> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNKDHW>)
+    {
+        return {0, 1, 2, 3, 4, 5};
+    }
+    if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNWC> ||
+                 std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKXC> ||
+                 std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNWK>)
+    {
+        return {0, 1, 3, 2};
+    }
+    else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNHWC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKYXC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNHWK>)
+    {
+        return {0, 1, 4, 2, 3};
+    }
+    else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNDHWC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GKZYXC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::GNDHWK>)
+    {
+        return {0, 1, 5, 2, 3, 4};
+    }
+    else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NWGC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::KXGC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NWGK>)
+    {
+        return {2, 0, 3, 1};
+    }
+    else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NHWGC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::KYXGC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NHWGK>)
+    {
+        return {3, 0, 4, 1, 2};
+    }
+    else if constexpr(std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NDHWGC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::KZYXGC> ||
+                      std::is_same_v<OldLayout, ck_tile::tensor_layout::convolution::NDHWGK>)
+    {
+        return {4, 0, 5, 1, 2, 3};
+    }
+    else
+    {
+        printf("%s\n", __func__);
+        throw std::runtime_error("wrong! unsupported layout");
+    }
+}
+
+} // namespace detail
+
+// make tensor descriptor for packed input tensor, and order the dimension in the order of GNCHW
+// regardless of physical layout
+template <typename InLayout>
+CK_TILE_HOST HostTensorDescriptor
+make_input_host_tensor_descriptor_g_n_c_wis_packed(const ck_tile::conv::ConvParam& param)
+{
+    std::vector<std::size_t> physical_lengths;
+
+    if constexpr(std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNCW> ||
+                 std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNCHW> ||
+                 std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNCDHW>)
+    {
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
+                                                    static_cast<std::size_t>(param.N_),
+                                                    static_cast<std::size_t>(param.C_)};
+
+        physical_lengths.insert(physical_lengths.end(),
+                                param.input_spatial_lengths_.begin(),
+                                param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    else if constexpr(std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNWC> ||
+                      std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNHWC> ||
+                      std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::GNDHWC>)
+    {
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
+                                                    static_cast<std::size_t>(param.N_),
+                                                    static_cast<std::size_t>(param.C_)};
+
+        physical_lengths.insert(physical_lengths.begin() + 2,
+                                param.input_spatial_lengths_.begin(),
+                                param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    else if constexpr(std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::NWGC> ||
+                      std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::NHWGC> ||
+                      std::is_same_v<InLayout, ck_tile::tensor_layout::convolution::NDHWGC>)
+    {
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.N_),
+                                                    static_cast<std::size_t>(param.G_),
+                                                    static_cast<std::size_t>(param.C_)};
+
+        physical_lengths.insert(physical_lengths.begin() + 1,
+                                param.input_spatial_lengths_.begin(),
+                                param.input_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    else
+    {
+        printf("%s\n", __func__);
+        printf("%s\n", InLayout::name);
+        throw std::runtime_error("wrong! unsupported layout");
+    }
+
+    return transpose_host_tensor_descriptor_given_new2old(
+        HostTensorDescriptor(physical_lengths),
+        detail::get_layout_transpose_gnchw_to_old<InLayout>());
+}
+
+// make tensor descriptor for packed weight tensor, and order the dimension in the order of GKCYX
+// regardless of physical layout
+template <typename WeiLayout>
+CK_TILE_HOST HostTensorDescriptor
+make_weight_host_tensor_descriptor_g_k_c_xs_packed(const ck_tile::conv::ConvParam& param)
+{
+    std::vector<std::size_t> physical_lengths;
+
+    if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KXC> ||
+                 std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KYXC> ||
+                 std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KZYXC>)
+    {
+        if(param.G_ != 1)
+        {
+            throw std::runtime_error("wrong! G != 1");
+        }
+
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.K_),
+                                                    static_cast<std::size_t>(param.C_)};
+
+        physical_lengths.insert(physical_lengths.end(),
+                                param.filter_spatial_lengths_.begin(),
+                                param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    else if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKCX> ||
+                      std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKCYX> ||
+                      std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKCZYX>)
+    {
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
+                                                    static_cast<std::size_t>(param.K_),
+                                                    static_cast<std::size_t>(param.C_)};
+
+        physical_lengths.insert(physical_lengths.end(),
+                                param.filter_spatial_lengths_.begin(),
+                                param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    else if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKXC> ||
+                      std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKYXC> ||
+                      std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::GKZYXC>)
+    {
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
+                                                    static_cast<std::size_t>(param.K_),
+                                                    static_cast<std::size_t>(param.C_)};
+
+        physical_lengths.insert(physical_lengths.begin() + 2,
+                                param.filter_spatial_lengths_.begin(),
+                                param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    else if constexpr(std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KXGC> ||
+                      std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KYXGC> ||
+                      std::is_same_v<WeiLayout, ck_tile::tensor_layout::convolution::KZYXGC>)
+    {
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.K_),
+                                                    static_cast<std::size_t>(param.G_),
+                                                    static_cast<std::size_t>(param.C_)};
+
+        physical_lengths.insert(physical_lengths.begin() + 1,
+                                param.filter_spatial_lengths_.begin(),
+                                param.filter_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    else
+    {
+        printf("%s\n", __func__);
+        printf("%s\n", WeiLayout::name);
+        throw std::runtime_error("wrong! unsupported layout");
+    }
+
+    return transpose_host_tensor_descriptor_given_new2old(
+        HostTensorDescriptor(physical_lengths),
+        detail::get_layout_transpose_gnchw_to_old<WeiLayout>());
+}
+
+// make tensor descriptor for packed output tensor, and order the dimension in the order of GNKHW
+// regardless of physical layout
+template <typename OutLayout>
+CK_TILE_HOST HostTensorDescriptor
+make_output_host_tensor_descriptor_g_n_k_wos_packed(const ck_tile::conv::ConvParam& param)
+{
+    std::vector<std::size_t> physical_lengths;
+
+    if constexpr(std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNKW> ||
+                 std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNKHW> ||
+                 std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNKDHW>)
+    {
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
+                                                    static_cast<std::size_t>(param.N_),
+                                                    static_cast<std::size_t>(param.K_)};
+
+        physical_lengths.insert(physical_lengths.end(),
+                                param.output_spatial_lengths_.begin(),
+                                param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    // separate from legacy code above
+    else if constexpr(std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNWK> ||
+                      std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNHWK> ||
+                      std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::GNDHWK>)
+    {
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.G_),
+                                                    static_cast<std::size_t>(param.N_),
+                                                    static_cast<std::size_t>(param.K_)};
+
+        physical_lengths.insert(physical_lengths.begin() + 2,
+                                param.output_spatial_lengths_.begin(),
+                                param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    else if constexpr(std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::NWGK> ||
+                      std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::NHWGK> ||
+                      std::is_same_v<OutLayout, ck_tile::tensor_layout::convolution::NDHWGK>)
+    {
+        physical_lengths = std::vector<std::size_t>{static_cast<std::size_t>(param.N_),
+                                                    static_cast<std::size_t>(param.G_),
+                                                    static_cast<std::size_t>(param.K_)};
+
+        physical_lengths.insert(physical_lengths.begin() + 1,
+                                param.output_spatial_lengths_.begin(),
+                                param.output_spatial_lengths_.begin() + param.num_dim_spatial_);
+    }
+    else
+    {
+        printf("%s\n", __func__);
+        printf("%s\n", OutLayout::name);
+        throw std::runtime_error("wrong! unsupported layout");
+    }
+
+    return transpose_host_tensor_descriptor_given_new2old(
+        HostTensorDescriptor(physical_lengths),
+        detail::get_layout_transpose_gnchw_to_old<OutLayout>());
+}
+
+} // namespace conv
+} // namespace ck_tile

include/ck_tile/host/convolution_parameter.hpp

@@ -0,0 +1,283 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <cstdlib>
+#include <numeric>
+#include <iterator>
+#include <vector>
+
+namespace ck_tile {
+namespace conv {
+
+struct ConvParam
+{
+    ConvParam();
+    ConvParam(ck_tile::index_t n_dim,
+              ck_tile::index_t group_count,
+              ck_tile::index_t n_batch,
+              ck_tile::index_t n_out_channels,
+              ck_tile::index_t n_in_channels,
+              const std::vector<ck_tile::index_t>& filters_len,
+              const std::vector<ck_tile::index_t>& input_len,
+              const std::vector<ck_tile::index_t>& strides,
+              const std::vector<ck_tile::index_t>& dilations,
+              const std::vector<ck_tile::index_t>& left_pads,
+              const std::vector<ck_tile::index_t>& right_pads)
+        : num_dim_spatial_(static_cast<ck_tile::long_index_t>(n_dim)),
+          G_(static_cast<ck_tile::long_index_t>(group_count)),
+          N_(static_cast<ck_tile::long_index_t>(n_batch)),
+          K_(static_cast<ck_tile::long_index_t>(n_out_channels)),
+          C_(static_cast<ck_tile::long_index_t>(n_in_channels)),
+          filter_spatial_lengths_(num_dim_spatial_),
+          input_spatial_lengths_(num_dim_spatial_),
+          output_spatial_lengths_(num_dim_spatial_),
+          conv_filter_strides_(num_dim_spatial_),
+          conv_filter_dilations_(num_dim_spatial_),
+          input_left_pads_(num_dim_spatial_),
+          input_right_pads_(num_dim_spatial_)
+    {
+        if(static_cast<ck_tile::index_t>(filter_spatial_lengths_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(input_spatial_lengths_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(conv_filter_strides_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(conv_filter_dilations_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(input_left_pads_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(input_right_pads_.size()) != num_dim_spatial_)
+        {
+            throw(std::runtime_error(
+                "ConvParam::ConvParam: "
+                "parameter size is different from number of declared dimensions!"));
+        }
+
+        for(ck_tile::index_t i = 0; i < num_dim_spatial_; ++i)
+        {
+            filter_spatial_lengths_[i] = static_cast<ck_tile::long_index_t>(filters_len[i]);
+            input_spatial_lengths_[i]  = static_cast<ck_tile::long_index_t>(input_len[i]);
+            conv_filter_strides_[i]    = static_cast<ck_tile::long_index_t>(strides[i]);
+            conv_filter_dilations_[i]  = static_cast<ck_tile::long_index_t>(dilations[i]);
+            input_left_pads_[i]        = static_cast<ck_tile::long_index_t>(left_pads[i]);
+            input_right_pads_[i]       = static_cast<ck_tile::long_index_t>(right_pads[i]);
+
+            // XEff = (X - 1) * conv_dilation_w + 1;
+            // Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
+            const ck_tile::long_index_t x_eff =
+                (filter_spatial_lengths_[i] - 1) * conv_filter_dilations_[i] + 1;
+
+            output_spatial_lengths_[i] =
+                (input_spatial_lengths_[i] + input_left_pads_[i] + input_right_pads_[i] - x_eff) /
+                    conv_filter_strides_[i] +
+                1;
+        }
+    }
+
+    ConvParam(ck_tile::long_index_t n_dim,
+              ck_tile::long_index_t group_count,
+              ck_tile::long_index_t n_batch,
+              ck_tile::long_index_t n_out_channels,
+              ck_tile::long_index_t n_in_channels,
+              const std::vector<ck_tile::long_index_t>& filters_len,
+              const std::vector<ck_tile::long_index_t>& input_len,
+              const std::vector<ck_tile::long_index_t>& strides,
+              const std::vector<ck_tile::long_index_t>& dilations,
+              const std::vector<ck_tile::long_index_t>& left_pads,
+              const std::vector<ck_tile::long_index_t>& right_pads)
+        : num_dim_spatial_(n_dim),
+          G_(group_count),
+          N_(n_batch),
+          K_(n_out_channels),
+          C_(n_in_channels),
+          filter_spatial_lengths_(filters_len),
+          input_spatial_lengths_(input_len),
+          output_spatial_lengths_(num_dim_spatial_),
+          conv_filter_strides_(strides),
+          conv_filter_dilations_(dilations),
+          input_left_pads_(left_pads),
+          input_right_pads_(right_pads)
+    {
+        if(static_cast<ck_tile::index_t>(filter_spatial_lengths_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(input_spatial_lengths_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(conv_filter_strides_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(conv_filter_dilations_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(input_left_pads_.size()) != num_dim_spatial_ ||
+           static_cast<ck_tile::index_t>(input_right_pads_.size()) != num_dim_spatial_)
+        {
+            throw(std::runtime_error(
+                "ConvParam::ConvParam: "
+                "parameter size is different from number of declared dimensions!"));
+        }
+
+        for(ck_tile::index_t i = 0; i < num_dim_spatial_; ++i)
+        {
+            // XEff = (X - 1) * conv_dilation_w + 1;
+            // Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
+            const ck_tile::long_index_t x_eff =
+                (filter_spatial_lengths_[i] - 1) * conv_filter_dilations_[i] + 1;
+
+            output_spatial_lengths_[i] =
+                (input_spatial_lengths_[i] + input_left_pads_[i] + input_right_pads_[i] - x_eff) /
+                    conv_filter_strides_[i] +
+                1;
+        }
+    }
+
+    ck_tile::long_index_t num_dim_spatial_;
+    ck_tile::long_index_t G_;
+    ck_tile::long_index_t N_;
+    ck_tile::long_index_t K_;
+    ck_tile::long_index_t C_;
+
+    std::vector<ck_tile::long_index_t> filter_spatial_lengths_;
+    std::vector<ck_tile::long_index_t> input_spatial_lengths_;
+    std::vector<ck_tile::long_index_t> output_spatial_lengths_;
+
+    std::vector<ck_tile::long_index_t> conv_filter_strides_;
+    std::vector<ck_tile::long_index_t> conv_filter_dilations_;
+
+    std::vector<ck_tile::long_index_t> input_left_pads_;
+    std::vector<ck_tile::long_index_t> input_right_pads_;
+
+    std::vector<ck_tile::long_index_t> GetOutputSpatialLengths() const
+    {
+        return output_spatial_lengths_;
+    }
+
+    std::size_t GetFlops() const
+    {
+        // 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product>
+        return static_cast<std::size_t>(2) * G_ * N_ * K_ * C_ *
+               std::accumulate(std::begin(output_spatial_lengths_),
+                               std::next(std::begin(output_spatial_lengths_), num_dim_spatial_),
+                               1,
+                               std::multiplies<>()) *
+               std::accumulate(std::begin(filter_spatial_lengths_),
+                               std::next(std::begin(filter_spatial_lengths_), num_dim_spatial_),
+                               1,
+                               std::multiplies<>());
+    }
+
+    template <typename InDataType>
+    std::size_t GetInputByte() const
+    {
+        // sizeof(InDataType) * (G * N * C * <input spatial lengths product>) +
+        return sizeof(InDataType) *
+               (G_ * N_ * C_ *
+                std::accumulate(std::begin(input_spatial_lengths_),
+                                std::next(std::begin(input_spatial_lengths_), num_dim_spatial_),
+                                1,
+                                std::multiplies<>()));
+    }
+
+    template <typename WeiDataType>
+    std::size_t GetWeightByte() const
+    {
+        // sizeof(WeiDataType) * (G * K * C * <filter spatial lengths product>) +
+        return sizeof(WeiDataType) *
+               (G_ * K_ * C_ *
+                std::accumulate(std::begin(filter_spatial_lengths_),
+                                std::next(std::begin(filter_spatial_lengths_), num_dim_spatial_),
+                                1,
+                                std::multiplies<>()));
+    }
+
+    template <typename OutDataType>
+    std::size_t GetOutputByte() const
+    {
+        // sizeof(OutDataType) * (G * N * K * <output spatial lengths product>);
+        return sizeof(OutDataType) * (G_ * N_ * K_ *
+                                      std::accumulate(std::begin(output_spatial_lengths_),
+                                                      std::end(output_spatial_lengths_),
+                                                      static_cast<std::size_t>(1),
+                                                      std::multiplies<std::size_t>()));
+    }
+
+    template <typename InDataType, typename WeiDataType, typename OutDataType>
+    std::size_t GetByte() const
+    {
+        return GetInputByte<InDataType>() + GetWeightByte<WeiDataType>() +
+               GetOutputByte<OutDataType>();
+    }
+};
+
+ConvParam::ConvParam()
+    : ConvParam::ConvParam(2, 1, 128, 256, 192, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, {1, 1})
+{
+}
+
+CK_TILE_HOST std::string get_conv_param_parser_helper_msg()
+{
+    std::string msg;
+
+    msg += "Following arguments (depending on number of spatial dims):\n"
+           " Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d)\n"
+           " G, N, K, C, \n"
+           " <filter spatial dimensions>, (ie Y, X for 2D)\n"
+           " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
+           " <strides>, (ie Sy, Sx for 2D)\n"
+           " <dilations>, (ie Dy, Dx for 2D)\n"
+           " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
+           " <right padding>, (ie RightPy, RightPx for 2D)\n";
+
+    return msg;
+}
+
+CK_TILE_HOST ck_tile::conv::ConvParam
+parse_conv_param(int num_dim_spatial, int arg_idx, char* const argv[])
+{
+    const ck_tile::long_index_t G = std::stol(argv[arg_idx++]);
+    const ck_tile::long_index_t N = std::stol(argv[arg_idx++]);
+    const ck_tile::long_index_t K = std::stol(argv[arg_idx++]);
+    const ck_tile::long_index_t C = std::stol(argv[arg_idx++]);
+
+    std::vector<ck_tile::long_index_t> filter_spatial_lengths(num_dim_spatial);
+    std::vector<ck_tile::long_index_t> input_spatial_lengths(num_dim_spatial);
+    std::vector<ck_tile::long_index_t> conv_filter_strides(num_dim_spatial);
+    std::vector<ck_tile::long_index_t> conv_filter_dilations(num_dim_spatial);
+    std::vector<ck_tile::long_index_t> input_left_pads(num_dim_spatial);
+    std::vector<ck_tile::long_index_t> input_right_pads(num_dim_spatial);
+
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        filter_spatial_lengths[i] = std::stol(argv[arg_idx++]);
+    }
+
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        input_spatial_lengths[i] = std::stol(argv[arg_idx++]);
+    }
+
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        conv_filter_strides[i] = std::stol(argv[arg_idx++]);
+    }
+
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        conv_filter_dilations[i] = std::stol(argv[arg_idx++]);
+    }
+
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        input_left_pads[i] = std::stol(argv[arg_idx++]);
+    }
+
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        input_right_pads[i] = std::stol(argv[arg_idx++]);
+    }
+
+    return ck_tile::conv::ConvParam{num_dim_spatial,
+                                    G,
+                                    N,
+                                    K,
+                                    C,
+                                    filter_spatial_lengths,
+                                    input_spatial_lengths,
+                                    conv_filter_strides,
+                                    conv_filter_dilations,
+                                    input_left_pads,
+                                    input_right_pads};
+}
+
+} // namespace conv
+} // namespace ck_tile

include/ck_tile/host/host_tensor.hpp

@@ -176,7 +176,20 @@ struct HostTensorDescriptor
         return std::inner_product(iss.begin(), iss.end(), mStrides.begin(), std::size_t{0});
     }
 
-    friend std::ostream& operator<<(std::ostream& os, const HostTensorDescriptor& desc);
+    friend std::ostream& operator<<(std::ostream& os, const HostTensorDescriptor& desc)
+    {
+        os << "dim " << desc.get_num_of_dimension() << ", ";
+
+        os << "lengths {";
+        LogRange(os, desc.get_lengths(), ", ");
+        os << "}, ";
+
+        os << "strides {";
+        LogRange(os, desc.get_strides(), ", ");
+        os << "}";
+
+        return os;
+    }
 
     private:
     std::vector<std::size_t> mLens;

include/ck_tile/host/reference/reference_im2col.hpp

@@ -1,5 +1,5 @@
 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -9,53 +9,125 @@
 
 namespace ck_tile {
 
-template <typename T>
-CK_TILE_HOST void reference_im2col(HostTensor<T>& in_mtx_host_ref,
-                                   const HostTensor<T>& in_host,
-                                   int /*N*/,
-                                   int /*K*/,
-                                   int C,
-                                   int /*Y*/,
-                                   int X,
-                                   int Hi,
-                                   int Wi,
-                                   int Ho,
-                                   int Wo,
-                                   int ConvStrideH,
-                                   int ConvStrideW,
-                                   int ConvDilationH,
-                                   int ConvDilationW,
-                                   int InLeftPadH,
-                                   int InLeftPadW,
-                                   int /*InRightPadH*/,
-                                   int /*InRightPadW*/)
+template <typename InDataType, typename OutDataType, index_t NDimSpatial>
+CK_TILE_HOST void reference_im2col(const HostTensor<InDataType>& in_host,
+                                   HostTensor<OutDataType>& out_host,
+                                   const ck_tile::conv::ConvParam& conv_params)
 {
-    int GemmM = in_mtx_host_ref.get_lengths()[0];
-    int GemmK = in_mtx_host_ref.get_lengths()[1];
+    const long_index_t G = in_host.get_lengths()[0];
+    const long_index_t N = in_host.get_lengths()[1];
+    const long_index_t C = in_host.get_lengths()[2];
 
-    for(int gemm_m = 0; gemm_m < GemmM; ++gemm_m)
+    if constexpr(NDimSpatial == 1)
     {
-        int mtmp = gemm_m;
-        int n    = mtmp / (Ho * Wo);
-        mtmp -= n * Ho * Wo;
-        int ho = mtmp / Wo;
-        int wo = mtmp - ho * Wo;
+        const long_index_t Wo = conv_params.output_spatial_lengths_[0];
+        auto func             = [&](auto g, auto n, auto wo) {
+            long_index_t row    = n * Wo + wo;
+            long_index_t column = 0;
 
-        for(int gemm_k = 0; gemm_k < GemmK; ++gemm_k)
-        {
-            int ktmp = gemm_k;
-            int y    = ktmp / (X * C);
-            ktmp -= y * X * C;
-            int x = ktmp / C;
-            int c = ktmp - x * C;
+            for(long_index_t x = 0; x < conv_params.filter_spatial_lengths_[0]; ++x)
+            {
+                auto wi = static_cast<long_index_t>(wo * conv_params.conv_filter_strides_[0]) +
+                          static_cast<long_index_t>(x * conv_params.conv_filter_dilations_[0]) -
+                          static_cast<long_index_t>(conv_params.input_left_pads_[0]);
 
-            int hi = y * ConvDilationH + ho * ConvStrideH - InLeftPadH;
-            int wi = x * ConvDilationW + wo * ConvStrideW - InLeftPadW;
+                for(long_index_t c = 0; c < C; ++c)
+                {
+                    if(wi >= 0 && type_convert<std::size_t>(wi) < in_host.get_lengths()[3])
+                    {
+                        InDataType v_in          = in_host(g, n, c, wi);
+                        out_host(g, row, column) = type_convert<OutDataType>(v_in);
+                    }
+                    column++;
+                }
+            }
+        };
 
-            bool inbound = (hi >= 0 && hi < Hi && wi >= 0 && wi < Wi);
+        make_ParallelTensorFunctor(func, G, N, Wo)(std::thread::hardware_concurrency());
+    }
+    else if constexpr(NDimSpatial == 2)
+    {
+        const long_index_t Ho = conv_params.output_spatial_lengths_[0];
+        const long_index_t Wo = conv_params.output_spatial_lengths_[1];
 
-            in_mtx_host_ref(gemm_m, gemm_k) = inbound ? in_host(n, hi, wi, c) : 0;
-        }
+        auto func = [&](auto g, auto n, auto ho, auto wo) {
+            long_index_t row    = n * Ho * Wo + ho * Wo + wo;
+            long_index_t column = 0;
+
+            for(long_index_t y = 0; y < conv_params.filter_spatial_lengths_[0]; ++y)
+            {
+                auto hi = static_cast<long_index_t>(ho * conv_params.conv_filter_strides_[0]) +
+                          static_cast<long_index_t>(y * conv_params.conv_filter_dilations_[0]) -
+                          static_cast<long_index_t>(conv_params.input_left_pads_[0]);
+
+                for(long_index_t x = 0; x < conv_params.filter_spatial_lengths_[1]; ++x)
+                {
+                    auto wi = static_cast<long_index_t>(wo * conv_params.conv_filter_strides_[1]) +
+                              static_cast<long_index_t>(x * conv_params.conv_filter_dilations_[1]) -
+                              static_cast<long_index_t>(conv_params.input_left_pads_[1]);
+
+                    for(long_index_t c = 0; c < C; ++c)
+                    {
+
+                        if(hi >= 0 && type_convert<std::size_t>(hi) < in_host.get_lengths()[3] &&
+                           wi >= 0 && type_convert<std::size_t>(wi) < in_host.get_lengths()[4])
+                        {
+                            InDataType v_in          = in_host(g, n, c, hi, wi);
+                            out_host(g, row, column) = type_convert<OutDataType>(v_in);
+                        }
+                        column++;
+                    }
+                }
+            }
+        };
+
+        make_ParallelTensorFunctor(func, G, N, Ho, Wo)(std::thread::hardware_concurrency());
+    }
+    else if constexpr(NDimSpatial == 3)
+    {
+        const long_index_t Do = conv_params.output_spatial_lengths_[0];
+        const long_index_t Ho = conv_params.output_spatial_lengths_[1];
+        const long_index_t Wo = conv_params.output_spatial_lengths_[2];
+
+        auto func = [&](auto g, auto n, auto d_o, auto ho, auto wo) {
+            long_index_t row    = n * Do * Ho * Wo + d_o * Ho * Wo + ho * Wo + wo;
+            long_index_t column = 0;
+
+            for(long_index_t z = 0; z < conv_params.filter_spatial_lengths_[0]; ++z)
+            {
+                auto di = static_cast<long_index_t>(d_o * conv_params.conv_filter_strides_[0]) +
+                          static_cast<long_index_t>(z * conv_params.conv_filter_dilations_[0]) -
+                          static_cast<long_index_t>(conv_params.input_left_pads_[0]);
+                for(long_index_t y = 0; y < conv_params.filter_spatial_lengths_[1]; ++y)
+                {
+                    auto hi = static_cast<long_index_t>(ho * conv_params.conv_filter_strides_[1]) +
+                              static_cast<long_index_t>(y * conv_params.conv_filter_dilations_[1]) -
+                              static_cast<long_index_t>(conv_params.input_left_pads_[1]);
+                    for(long_index_t x = 0; x < conv_params.filter_spatial_lengths_[2]; ++x)
+                    {
+                        auto wi =
+                            static_cast<long_index_t>(wo * conv_params.conv_filter_strides_[2]) +
+                            static_cast<long_index_t>(x * conv_params.conv_filter_dilations_[2]) -
+                            static_cast<long_index_t>(conv_params.input_left_pads_[2]);
+                        for(long_index_t c = 0; c < C; ++c)
+                        {
+                            if(di >= 0 &&
+                               type_convert<std::size_t>(di) < in_host.get_lengths()[3] &&
+                               hi >= 0 &&
+                               type_convert<std::size_t>(hi) < in_host.get_lengths()[4] &&
+                               wi >= 0 && type_convert<std::size_t>(wi) < in_host.get_lengths()[5])
+                            {
+                                InDataType v_in          = in_host(g, n, c, di, hi, wi);
+                                out_host(g, row, column) = type_convert<OutDataType>(v_in);
+                            }
+                            column++;
+                        }
+                    }
+                }
+            }
+        };
+
+        make_ParallelTensorFunctor(func, G, N, Do, Ho, Wo)(std::thread::hardware_concurrency());
     }
 }
 } // namespace ck_tile