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/*
* Copyright (c) 2022 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.
*/
#ifdef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION
#include "src/core/experimental/dynamic_fusion/ClKernelBuildingImpl/components/ClFloorKernelComponent.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Validate.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
namespace arm_compute
{
namespace experimental
{
namespace dynamic_fusion
{
ComponentType ClFloorKernelComponent::get_component_type() const
{
return ComponentType::Simple;
}
std::set<std::string> ClFloorKernelComponent::get_headers_list() const
{
return std::set<std::string> { "common/experimental/gemm_fused_post_ops/fp_mixed_precision_helpers.h", "tile_helpers.h" };
}
Window ClFloorKernelComponent::get_window() const
{
const ITensorInfo *src_info = _blueprint->impl().get_kernel_argument_info(_src.arg_id);
ITensorInfo *dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());
ARM_COMPUTE_ERROR_ON_NULLPTR(src_info, dst_info);
auto_init_if_empty(*dst_info, src_info->tensor_shape(), 1, src_info->data_type());
TensorShape output_shape = dst_info->tensor_shape();
// Collapse Dim 1 (W) and Dim 2 (H) together, leave Dim 0 (C) and upper dimensions unchanged
// This is in line with the collapsing convention used by Conv2d
output_shape.collapse(2U, 1U);
const unsigned int vector_size_byte_opencl = 16;
const unsigned int num_elems_processed_per_iteration = adjust_vec_size(vector_size_byte_opencl / dst_info->element_size(), dst_info->dimension(0));
Window win = calculate_max_window(output_shape, Steps(num_elems_processed_per_iteration));
return win;
}
std::string ClFloorKernelComponent::get_component_code() const
{
bool is_root = _blueprint->impl().group(_src.arg_id) == SharedVarGroup::Argument;
if(is_root)
{
return R"_(
//------------------ START KERNEL {{meta_kernel_id}} FLOOR ---------------------
// IN_0(src) {{src}}
// OUT(dst, accum) {{dst}}
TILE({{DATA_TYPE}}, M0, N0, {{dst}});
{
TILE({{DATA_TYPE}}, M0, N0, src_tile);
// Since mout maps to dimensions 1 (y) and dimension 2 (z) of the input tensor because of the collapsed window, bout maps to dimension 3 (w)
{{src}}_offset_first_element_in_bytes += bout * {{src}}_stride_w;
T_LOAD({{DATA_TYPE}}, M0, N0, BUFFER, {{src}}, cout, mout, 1, {{src}}_stride_y, src_tile);
T_FLOOR({{DATA_TYPE}}, M0, N0, src_tile, {{dst}});
}
//------------------ END KERNEL {{meta_kernel_id}} FLOOR ---------------------
)_";
}
else
{
return R"_(
//------------------ START KERNEL {{meta_kernel_id}} FLOOR ---------------------
// IN_0/Out(Accumulator) {{acc}}
// output = floor(input)
{
T_FLOOR({{DATA_TYPE}}, M0, N0, {{acc}}, {{acc}});
}
//------------------ END KERNEL {{meta_kernel_id}} FLOOR ---------------------
)_";
}
}
CLBuildOptions ClFloorKernelComponent::generate_build_options() const
{
CLBuildOptions build_opts{};
const auto n0 = _blueprint->impl().get_execution_window().x().step();
const auto m0 = _blueprint->impl().get_execution_window().y().step();
const auto dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());
const unsigned int partial_store_n0 = dst_info->dimension(0) % n0;
build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0));
return build_opts;
}
std::string ClFloorKernelComponent::generate_config_id() const
{
auto t_dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());
std::string config_id{};
config_id += lower_string(string_from_data_type(t_dst_info->data_type()));
config_id += "_";
config_id += support::cpp11::to_string(t_dst_info->dimension(0));
config_id += "_";
config_id += support::cpp11::to_string(t_dst_info->dimension(1));
config_id += "_";
config_id += lower_string(string_from_data_layout(t_dst_info->data_layout()));
return config_id;
}
void ClFloorKernelComponent::allocate_shared_vars(SharedVarTable &vtable) const
{
vtable.add(_src, _blueprint->impl().group(_src.arg_id), ClKernelArgDescriptor(_src.arg_id, ClKernelTensorArgType::Tensor_4D_t_Buffer), "src");
vtable.add(_dst, _blueprint->impl().group(_dst.arg_id), ClKernelArgDescriptor(_dst.arg_id, ClKernelTensorArgType::Tensor_4D_t_Buffer), "dst");
}
ClFloorKernelComponent::TagLUT ClFloorKernelComponent::get_tag_lut(const SharedVarTable &vtable) const
{
TagLUT lut{};
const auto t_dst_info = _blueprint->impl().get_kernel_argument_info(_blueprint->impl().get_dst_id());
// Arguments and global shared variables
const bool is_root = _blueprint->impl().group(_src.arg_id) == SharedVarGroup::Argument;
if(is_root)
{
lut["src"] = vtable.get(_src);
lut["dst"] = vtable.get(_dst);
}
else
{
lut["acc"] = vtable.get(_src);
}
lut["meta_kernel_id"] = id();
lut["DATA_TYPE"] = get_cl_type_from_data_type(t_dst_info->data_type());
return lut;
}
} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute
#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
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