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/*
* Copyright (c) 2017-2021 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/NEON/functions/NEGEMMConvolutionLayer.h"
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/Tensor.h"
#include "src/core/helpers/MemoryHelpers.h"
#include "src/cpu/operators/CpuGemmConv2d.h"
using namespace arm_compute::experimental;
namespace arm_compute
{
struct NEGEMMConvolutionLayer::Impl
{
const ITensor *weights{ nullptr };
std::unique_ptr<cpu::CpuGemmConv2d> op{ nullptr };
ITensorPack run_pack{};
MemoryGroup memory_group{};
IWeightsManager *weights_manager{ nullptr };
MemoryRequirements aux_mem_req{};
WorkspaceData<Tensor> workspace_tensors{};
bool is_prepared{ false };
};
NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager, IWeightsManager *weights_manager)
: _impl(std::make_unique<Impl>())
{
_impl->weights_manager = weights_manager;
_impl->memory_group = MemoryGroup(memory_manager);
}
NEGEMMConvolutionLayer::~NEGEMMConvolutionLayer() = default;
void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math, unsigned int num_groups)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
_impl->weights = weights;
_impl->op = std::make_unique<cpu::CpuGemmConv2d>();
_impl->op->configure(input->info(), weights->info(), (biases != nullptr ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups);
_impl->run_pack =
{
{ TensorType::ACL_SRC_0, input },
{ TensorType::ACL_SRC_1, weights },
{ TensorType::ACL_SRC_2, biases },
{ TensorType::ACL_DST, output }
};
_impl->aux_mem_req = _impl->op->workspace();
_impl->workspace_tensors = manage_workspace<Tensor>(_impl->aux_mem_req, _impl->memory_group, _impl->run_pack, _impl->run_pack);
}
Status NEGEMMConvolutionLayer::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, bool enable_fast_math, unsigned int num_groups)
{
return cpu::CpuGemmConv2d::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info, enable_fast_math, num_groups);
}
void NEGEMMConvolutionLayer::run()
{
prepare();
MemoryGroupResourceScope scope_mg(_impl->memory_group);
_impl->op->run(_impl->run_pack);
}
void NEGEMMConvolutionLayer::prepare()
{
if(!_impl->is_prepared)
{
_impl->op->prepare(_impl->run_pack);
// Release temporary tensors that are only used in prepare stage
release_temporaries<Tensor>(_impl->aux_mem_req, _impl->workspace_tensors);
_impl->is_prepared = true;
}
}
} // namespace arm_compute
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