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/*
* Copyright (c) 2017-2021, 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 "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/PixelValue.h"
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "src/core/helpers/MemoryHelpers.h"
#include "src/gpu/cl/operators/ClGemmConv2d.h"
#include "support/Cast.h"
#include <cmath>
#include <memory>
#include <tuple>
namespace arm_compute
{
using namespace arm_compute::misc::shape_calculator;
using namespace arm_compute::utils::cast;
using namespace arm_compute::experimental;
struct CLGEMMConvolutionLayer::Impl
{
const ITensor *weights{nullptr};
std::unique_ptr<opencl::ClGemmConv2d> op{nullptr};
ITensorPack run_pack{};
ITensorPack prep_pack{};
MemoryGroup memory_group{};
IWeightsManager *weights_manager{nullptr};
MemoryRequirements aux_mem_req{};
WorkspaceData<CLTensor> workspace_tensors{};
bool is_prepared{false};
};
CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager,
IWeightsManager *weights_manager)
: _impl(std::make_unique<Impl>())
{
_impl->memory_group = MemoryGroup(memory_manager);
_impl->weights_manager = weights_manager;
}
CLGEMMConvolutionLayer::~CLGEMMConvolutionLayer() = default;
void CLGEMMConvolutionLayer::configure(const ICLTensor *input,
const ICLTensor *weights,
const ICLTensor *biases,
ICLTensor *output,
const PadStrideInfo &conv_info,
const WeightsInfo &weights_info,
const Size2D &dilation,
const ActivationLayerInfo &act_info,
unsigned int num_groups)
{
configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, weights_info,
dilation, act_info, num_groups);
}
void CLGEMMConvolutionLayer::configure(const CLCompileContext &compile_context,
const ICLTensor *input,
const ICLTensor *weights,
const ICLTensor *biases,
ICLTensor *output,
const PadStrideInfo &conv_info,
const WeightsInfo &weights_info,
const Size2D &dilation,
const ActivationLayerInfo &act_info,
unsigned int num_groups)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
_impl->weights = weights;
_impl->op = std::make_unique<opencl::ClGemmConv2d>();
const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups);
_impl->op->configure(compile_context, input->info(), weights->info(),
(biases != nullptr ? biases->info() : nullptr), output->info(), conv2d_info, weights_info);
_impl->run_pack = {{TensorType::ACL_SRC_0, input},
{TensorType::ACL_SRC_1, weights},
{TensorType::ACL_SRC_2, biases},
{TensorType::ACL_DST, output}};
_impl->prep_pack = {
{TensorType::ACL_SRC_1, weights},
{TensorType::ACL_SRC_2, biases},
};
_impl->aux_mem_req = _impl->op->workspace();
_impl->workspace_tensors =
manage_workspace<CLTensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->prep_pack);
}
Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input,
const ITensorInfo *weights,
const ITensorInfo *biases,
const ITensorInfo *output,
const PadStrideInfo &conv_info,
const WeightsInfo &weights_info,
const Size2D &dilation,
const ActivationLayerInfo &act_info,
unsigned int num_groups)
{
const Conv2dInfo conv2d_info = Conv2dInfo(conv_info, dilation, act_info, false, num_groups);
return opencl::ClGemmConv2d::validate(input, weights, biases, output, conv2d_info, weights_info);
}
void CLGEMMConvolutionLayer::run()
{
prepare();
MemoryGroupResourceScope scope_mg(_impl->memory_group);
_impl->op->run(_impl->run_pack);
}
void CLGEMMConvolutionLayer::prepare()
{
if (!_impl->is_prepared)
{
_impl->op->prepare(_impl->prep_pack);
auto has_reshape =
std::find_if(_impl->aux_mem_req.begin(), _impl->aux_mem_req.end(),
[](const MemoryInfo &m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
if (has_reshape != std::end(_impl->aux_mem_req))
{
_impl->weights->mark_as_unused();
}
else
{
// Pack the B matrix to be used as the underlying GEMM performs no reshapes
_impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->weights);
}
release_temporaries(_impl->aux_mem_req, _impl->workspace_tensors);
_impl->is_prepared = true;
}
}
} // namespace arm_compute
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