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/*
* Copyright (c) 2021 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* Copyright (c) 2017-2020 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 "arm_compute/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/CLKernelLibraryEx.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "support/StringSupport.h"
#include "src/core/CL/CLValidate.h"
#include "src/core/AccessWindowStatic.h"
#include "src/core/helpers/WindowHelpers.h"
using namespace arm_compute;
namespace
{
Status validate_arguments(const ITensorInfo *accum, const ITensorInfo *biases)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(accum);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() != 1);
return Status{};
}
std::pair<Status, Window>
validate_and_configure_window(ITensorInfo *accum, ITensorInfo *biases, GPUTarget gpu_target,
unsigned int &num_elems_processed_per_iteration)
{
// Select the vector size to use (8 for Bifrost; 16 for Midgard).
bool is_gpu_bifrost =
gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G76, GPUTarget::G51,
GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::G52, GPUTarget::G52LIT);
num_elems_processed_per_iteration = is_gpu_bifrost ? 8 : 16;
// Configure kernel window
Window win = calculate_max_window(*accum, Steps(num_elems_processed_per_iteration));
AccessWindowStatic biases_access(
biases, 0, 0, ceil_to_multiple(biases->dimension(0), num_elems_processed_per_iteration),
biases->dimension(1));
AccessWindowHorizontal accum_access(accum, 0, num_elems_processed_per_iteration);
bool window_changed = update_window_and_padding(win, biases_access, accum_access);
Status err = (window_changed)
? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!")
: Status{};
return std::make_pair(err, win);
}
} // namespace
CLGEMMMatrixAccumulateBiasesKernel::CLGEMMMatrixAccumulateBiasesKernel()
: _accum(nullptr), _biases(nullptr)
{
}
void CLGEMMMatrixAccumulateBiasesKernel::configure(ICLTensor *accum, const ICLTensor *biases)
{
configure(CLKernelLibrary::get().get_compile_context(), accum, biases);
}
void CLGEMMMatrixAccumulateBiasesKernel::configure(const CLCompileContext &compile_context,
ICLTensor *accum, const ICLTensor *biases)
{
ARM_COMPUTE_UNUSED(compile_context);
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(accum, biases);
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(accum->info(), biases->info()));
_biases = biases;
_accum = accum;
// Get the target gpu
GPUTarget gpu_target = get_target();
unsigned int vector_size = 0;
// Configure kernel window
auto win_config =
validate_and_configure_window(accum->info(), biases->info(), gpu_target, vector_size);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
ICLKernel::configure_internal(win_config.second);
// Add build options
CLBuildOptions build_opts;
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(accum->info()->data_type()));
build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
// Create kernel
_kernel = static_cast<cl::Kernel>(
CLKernelLibraryEx::get().create_kernel("gemm_accumulate_biases", build_opts.options()));
}
Status CLGEMMMatrixAccumulateBiasesKernel::validate(const ITensorInfo *accum,
const ITensorInfo *biases, GPUTarget gpu_target)
{
unsigned int num_elems_processed_per_iteration = 0;
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(accum, biases));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(accum->clone().get(),
biases->clone().get(), gpu_target,
num_elems_processed_per_iteration)
.first);
return Status{};
}
void CLGEMMMatrixAccumulateBiasesKernel::run(const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
Window accum_slice = window.first_slice_window_2D();
Window biases_slice(accum_slice);
biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1));
// Run kernel
do
{
// Set arguments
unsigned int idx = 0;
add_2D_tensor_argument(idx, _accum, accum_slice);
add_1D_tensor_argument(idx, _biases, biases_slice);
enqueue(queue, *this, accum_slice, lws_hint());
} while (window.slide_window_slice_2D(accum_slice));
}
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