Remove CKW prototype and Template Writer

Gpu code in dynamic fusion is now written by stable CKW. We do not need CKW protoype and the older writer implementation, i.e. TemplateWriter.

It also removes the need for the flag -DACL_INTERNAL_TEST_CKW_IN_DF to compile and test dynamic fusion operator.

Resolves: COMPMID-6715

Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Change-Id: I9f9453311e79d9be612bd4754240d832f98503e8
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11116
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 0 insertions, 393 deletions

diff --git a/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateDirectConv2d.cpp b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateDirectConv2d.cpp
deleted file mode 100644
index f6a7a58d1d..0000000000
--- a/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateDirectConv2d.cpp
+++ /dev/null
@@ -1,393 +0,0 @@
-/*
- * Copyright (c) 2022-2023 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "ClTemplateDirectConv2d.h"
-
-#include "arm_compute/core/utils/helpers/AdjustVecSize.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "arm_compute/core/utils/StringUtils.h"
-
-#include "src/core/helpers/WindowHelpers.h"
-#include "src/dynamic_fusion/sketch/gpu/components/cl/ClComponentDirectConv2d.h"
-#include "src/dynamic_fusion/sketch/gpu/GpuKernelComponentGroup.h"
-#include "support/StringSupport.h"
-
-namespace arm_compute
-{
-namespace experimental
-{
-namespace dynamic_fusion
-{
-ClTemplateDirectConv2d::ClTemplateDirectConv2d(ComponentId                      id,
-                                               const ArgumentPack<ITensorInfo> &tensors,
-                                               const Attributes                &attributes,
-                                               const Settings                  &settings)
-    : IGpuTemplateComponentWriter{id, tensors},
-      _src{},
-      _weight{},
-      _bias{},
-      _dst{},
-      _attributes{attributes},
-      _settings{settings}
-{
-    _src    = this->tensors().get_const_tensor(TensorType::ACL_SRC_0);
-    _weight = this->tensors().get_const_tensor(TensorType::ACL_SRC_1);
-    if (this->tensors().get_const_tensor(TensorType::ACL_SRC_2))
-    {
-        _bias = this->tensors().get_const_tensor(TensorType::ACL_SRC_2);
-    }
-    _dst = this->tensors().get_const_tensor(TensorType::ACL_DST_0);
-    ARM_COMPUTE_ERROR_ON_NULLPTR(_src, _weight, _dst);
-}
-
-std::string ClTemplateDirectConv2d::get_name() const
-{
-    return "direct_conv2d";
-}
-
-std::string ClTemplateDirectConv2d::get_component_code(const ComponentGroup &comp_group) const
-{
-    ARM_COMPUTE_UNUSED(comp_group);
-
-    const auto channel_idx   = get_data_layout_dimension_index(_src->data_layout(), DataLayoutDimension::CHANNEL);
-    const auto k0            = adjust_vec_size(_settings.direct_conv_descriptor().k0, _src->dimension(channel_idx));
-    const bool leftover_loop = (_src->dimension(channel_idx) % k0) != 0;
-
-    std::string code = R"_(
-//------------------ START KERNEL {{meta_kernel_id}} ---------------------
-// IN_0(src)            {{src}}
-// IN_1(wei)            {{weight}}
-)_";
-    if (_bias && _bias->has_valid_id())
-    {
-        code += R"_(
-// IN_1(bia)            {{bias}}
-)_";
-    }
-    code += R"_(
-// OUT(dst, accum)      {{dst}}
-
-TILE(uint, M0, 1, g_dst_indirect_y);
-
-{
-#define _IWEI_WIDTH {{WEI_WIDTH}}
-#define _IWEI_HEIGHT {{WEI_HEIGHT}}
-#define _ISRC_WIDTH {{SRC_WIDTH}}
-#define _ISRC_HEIGHT {{SRC_HEIGHT}}
-#define _ISRC_CHANNELS {{SRC_CHANNELS}}
-#define _IDST_WIDTH {{DST_WIDTH}}
-#define _IDST_HEIGHT {{DST_HEIGHT}}
-#define _IDST_CHANNELS {{DST_CHANNELS}}
-#define _IY_MULTIPLIER (_IWEI_WIDTH * _IWEI_HEIGHT)
-
-    TILE(int, M0, 1, xi);
-    TILE(int, M0, 1, yi);
-
-    // Convert the linear index to coordinate
-    LOOP_UNROLLING(int, i, 0, 1, M0,
-    {
-        xi[0].s[i] = ((g_ind_1 + i) % _IDST_WIDTH) * {{STRIDE_X}};
-        yi[0].s[i] = ((g_ind_1 + i) / _IDST_WIDTH) * {{STRIDE_Y}};
-        xi[0].s[i] -= {{PAD_LEFT}};
-        yi[0].s[i] -= {{PAD_TOP}};
-    })
-
-    LOOP_UNROLLING(int, i, 0, 1, M0,
-    {
-        {{dst}}[i].v = 0;
-    })
-
-    for(int i = 0; i < (_IWEI_WIDTH * _IWEI_HEIGHT); ++i)
-    {
-        int xk = i % _IWEI_WIDTH;
-        int yk = i / _IWEI_WIDTH;
-
-        TILE(int, 1, M0, my);
-
-        LOOP_UNROLLING(int, i, 0, 1, M0,
-        {
-            int x_s    = xi[0].s[i] + xk;
-            int y_s    = yi[0].s[i] + yk;
-            my[0].s[i] = x_s + y_s *_ISRC_WIDTH;
-            my[0].s[i] = my[0].s[i] + g_ind_2 * (int)(_ISRC_WIDTH * _ISRC_HEIGHT);
-            my[0].s[i] = select(-1, my[0].s[i], x_s >= 0);
-            my[0].s[i] = select(-1, my[0].s[i], x_s < _ISRC_WIDTH);
-            my[0].s[i] = select(-1, my[0].s[i], y_s >= 0);
-            my[0].s[i] = select(-1, my[0].s[i], y_s < _ISRC_HEIGHT);
-        })
-
-        int ck = 0;
-        for(; ck <= (_ISRC_CHANNELS - K0); ck += K0)
-        {
-            TILE({{SRC_DATA_TYPE}}, M0, K0, a);
-            TILE({{WEI_DATA_TYPE}}, N0, K0, b);
-
-            LOOP_UNROLLING(int, i, 0, 1, M0,
-            {
-                a[i].v = {{ZERO_VALUE}};
-            })
-
-            LOOP_UNROLLING(int, i, 0, 1, N0,
-            {
-                b[i].v = {{ZERO_VALUE}};
-            })
-
-            T_LOAD2D_INDIRECT({{SRC_DATA_TYPE}}, M0, K0, {{SRC_TENSOR_TYPE}}, {{src}}, ck, {{src}}_stride_y, my, a);
-
-            T_LOAD({{WEI_DATA_TYPE}}, N0, K0, {{WEI_TENSOR_TYPE}}, {{weight}}, ck, g_ind_0 * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b);
-
-            T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, K0, NT, T, a, b, {{dst}});
-        }
-)_";
-
-    if (leftover_loop)
-    {
-        code += R"_(
-        for(; ck < _ISRC_CHANNELS; ++ck)
-        {
-            TILE({{SRC_DATA_TYPE}}, M0, 1, a);
-            TILE({{WEI_DATA_TYPE}}, N0, 1, b);
-
-            LOOP_UNROLLING(int, i, 0, 1, M0,
-            {
-                a[i].v = {{ZERO_VALUE}};
-            })
-
-            LOOP_UNROLLING(int, i, 0, 1, N0,
-            {
-                b[i].v = {{ZERO_VALUE}};
-            })
-
-            T_LOAD2D_INDIRECT({{SRC_DATA_TYPE}}, M0, 1, {{SRC_TENSOR_TYPE}}, {{src}}, ck, {{src}}_stride_y, my, a);
-
-            T_LOAD({{WEI_DATA_TYPE}}, N0, 1, BUFFER, {{weight}}, ck, g_ind_0 * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b);
-
-            T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, 1, NT, T, a, b, {{dst}});
-        }
-    )_";
-    }
-
-    code += R"_(
-#undef _I_WEI_WIDTH
-#undef _I_WEI_HEIGHT
-#undef _ISRC_WIDTH
-#undef _ISRC_HEIGHT
-#undef _ISRC_CHANNELS
-#undef _IDST_WIDTH
-#undef _IDST_HEIGHT
-#undef _IDST_CHANNELS
-#undef _IY_MULTIPLIER
-
-    }
-)_";
-
-    if (_bias && _bias->has_valid_id())
-    {
-        code += R"_(
-        TILE({{BIA_DATA_TYPE}}, 1, N0, bias0);
-
-        T_LOAD({{BIA_DATA_TYPE}}, 1, N0, BUFFER, {{bias}}, g_ind_0, 0, 1, 0, bias0);
-
-        T_ELTWISE_BROADCAST_ADD_X({{ACC_DATA_TYPE}}, M0, N0, {{dst}}, bias0, {{dst}});
-    )_";
-    }
-
-    code += R"_(
-    LOOP_UNROLLING(int, i, 0, 1, M0,
-    {
-        g_dst_indirect_y[i].v = (uint)min(g_ind_1 + i, (int)({{DST_WIDTH}} * {{DST_HEIGHT}}) - 1);
-        g_dst_indirect_y[i].v += g_ind_2 * (int)({{DST_WIDTH}} * {{DST_HEIGHT}});
-    })
-}
-//------------------ END KERNEL {{meta_kernel_id}} ---------------------
-)_";
-    return code;
-}
-
-void ClTemplateDirectConv2d::declare_variables(GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const
-{
-    vtable.declare_variable(comp_group, _src, GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer),
-                            "src");
-
-    const GpuKernelArgumentInfo::Type weight_type = _settings.export_to_cl_image()
-                                                        ? GpuKernelArgumentInfo::Type::Tensor_4D_t_Image
-                                                        : GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer;
-    vtable.declare_variable(comp_group, _weight, GpuKernelArgumentInfo(weight_type), "weight");
-
-    if (_bias && _bias->has_valid_id()) // optional bias
-    {
-        vtable.declare_variable(comp_group, _bias, GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Vector), "bias");
-    }
-    vtable.declare_variable(comp_group, _dst, GpuKernelArgumentInfo(common_tensor_type), "dst");
-}
-
-TagLUT ClTemplateDirectConv2d::get_tag_lut(const GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const
-{
-    TagLUT lut{};
-    // Arguments and global shared variables
-    lut["src"]    = vtable.get_variable(_src);
-    lut["weight"] = vtable.get_variable(_weight);
-
-    if (_bias && _bias->has_valid_id()) // optional bias
-    {
-        lut["bias"]          = vtable.get_variable(_bias);
-        lut["BIA_DATA_TYPE"] = get_cl_type_from_data_type(_bias->data_type());
-    }
-    lut["dst"] = vtable.get_variable(_dst);
-
-    const auto dst_argument = vtable.get_variable(comp_group.get_any_dst_tensor());
-    lut["arg_dst"]          = dst_argument.uniq_name;
-
-    // Local build options
-    lut["meta_kernel_id"] = id();
-    lut["ACC_DATA_TYPE"]  = _src->data_type();
-    lut["SRC_DATA_TYPE"]  = _src->data_type();
-    lut["WEI_DATA_TYPE"]  = _weight->data_type();
-
-    lut["SRC_TENSOR_TYPE"] = "BUFFER";
-    switch (vtable.get_variable(_weight).kernel_argument_info.type)
-    {
-        case GpuKernelArgumentInfo::Type::Image_Export_To_ClImage2D:
-        case GpuKernelArgumentInfo::Type::Image_3D_Export_To_ClImage2D:
-        case GpuKernelArgumentInfo::Type::Tensor_4D_t_Image:
-        {
-            lut["WEI_TENSOR_TYPE"] = "IMAGE";
-            break;
-        }
-        default:
-        {
-            lut["WEI_TENSOR_TYPE"] = "BUFFER";
-            break;
-        }
-    }
-    const auto width_idx   = 1;
-    const auto height_idx  = 2;
-    const auto channel_idx = 0;
-
-    lut["SRC_WIDTH"]    = _src->dimension(width_idx);
-    lut["SRC_HEIGHT"]   = _src->dimension(height_idx);
-    lut["SRC_CHANNELS"] = _src->dimension(channel_idx);
-
-    lut["WEI_WIDTH"]  = _weight->dimension(width_idx);
-    lut["WEI_HEIGHT"] = _weight->dimension(height_idx);
-
-    lut["DST_WIDTH"]    = _dst->dimension(width_idx);
-    lut["DST_HEIGHT"]   = _dst->dimension(height_idx);
-    lut["DST_CHANNELS"] = _dst->dimension(channel_idx);
-
-    lut["STRIDE_X"] = _attributes.stride().x();
-    lut["STRIDE_Y"] = _attributes.stride().y();
-
-    lut["PAD_LEFT"] = _attributes.pad().left;
-    lut["PAD_TOP"]  = _attributes.pad().top;
-
-    lut["ZERO_VALUE"] = 0;
-
-    return lut;
-}
-
-CLBuildOptions ClTemplateDirectConv2d::get_build_options(const ComponentGroup &comp_group) const
-{
-    const unsigned int channel_idx = get_data_layout_dimension_index(_src->data_layout(), DataLayoutDimension::CHANNEL);
-
-    const auto         root_window = comp_group.get_root_component()->template_writer()->get_window();
-    const unsigned int n0          = root_window.x().step();
-    const unsigned int m0          = root_window.y().step();
-    const unsigned int k0 = adjust_vec_size(_settings.direct_conv_descriptor().k0, _src->dimension(channel_idx));
-    const unsigned int partial_store_n0 = _dst->dimension(0) % n0;
-
-    CLBuildOptions build_opts{};
-    if (_settings.fast_relaxed_math())
-    {
-        build_opts.add_option("-cl-fast-relaxed-math");
-    }
-    else
-    {
-        // -cl-fast-relaxed-math also sets -cl-finite-math-only and -cl-unsafe-math-optimizations
-        // to disable -cl-finite-math-only, we only include -cl-unsafe-math-optimizations
-        build_opts.add_option("-cl-unsafe-math-optimizations");
-    }
-
-    build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
-    build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
-    build_opts.add_option("-DK0=" + support::cpp11::to_string(k0));
-    build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0));
-
-    return build_opts;
-}
-
-std::string ClTemplateDirectConv2d::get_config_id() const
-{
-    const DataType   data_type   = _src->data_type();
-    const DataLayout data_layout = _src->data_layout();
-
-    const unsigned int width_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-    const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
-
-    const unsigned int kernel_size = _weight->dimension(width_idx);
-
-    std::string config_id{};
-    config_id += lower_string(string_from_data_type(data_type));
-    config_id += "_";
-    config_id += support::cpp11::to_string(kernel_size);
-    config_id += "_";
-    config_id += support::cpp11::to_string(_attributes.stride().x());
-    config_id += "_";
-    config_id += support::cpp11::to_string(_attributes.stride().y());
-    config_id += "_";
-    config_id += support::cpp11::to_string(_dst->dimension(width_idx));
-    config_id += "_";
-    config_id += support::cpp11::to_string(_dst->dimension(height_idx));
-    config_id += "_";
-    config_id += lower_string(string_from_data_layout(data_layout));
-    return config_id;
-}
-
-std::set<std::string> ClTemplateDirectConv2d::get_headers_list() const
-{
-    return std::set<std::string>{"helpers.h", "tile_helpers.h"};
-}
-
-Window ClTemplateDirectConv2d::get_window() const
-{
-    ARM_COMPUTE_ERROR_ON_MSG(_dst->tensor_shape().total_size() == 0U, "Destination tensor is not initialized");
-
-    const auto output_shape = _dst->tensor_shape();
-    const auto desc         = _settings.direct_conv_descriptor();
-
-    const unsigned int n0 = adjust_vec_size(desc.n0, output_shape[0]);
-    const unsigned int m0 = adjust_vec_size(desc.m0, output_shape[1] * output_shape[2]);
-
-    // Create and configure kernel window
-    Window win = calculate_max_window(output_shape, Steps(n0, m0));
-
-    const size_t dim_y_collapsed = ceil_to_multiple(output_shape[1] * output_shape[2], m0);
-    win.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, m0));
-    win.set(Window::DimZ, Window::Dimension(0, output_shape.total_size_upper(3), 1));
-
-    return win;
-}
-
-} // namespace dynamic_fusion
-} // namespace experimental
-} // namespace arm_compute