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/*
* Copyright (c) 2017-2023 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/CLDeconvolutionLayer.h"
#include "arm_compute/core/Types.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/common/utils/Log.h"
#include "src/core/CL/ICLKernel.h"
#include "src/gpu/cl/IClOperator.h"
#include "src/gpu/cl/operators/ClTransposedConvolution.h"
#include <cmath>
#include <memory>
#include <tuple>
using namespace arm_compute;
using namespace arm_compute::misc::shape_calculator;
struct CLDeconvolutionLayer::Impl
{
const ICLTensor *src{nullptr};
const ICLTensor *weights{nullptr};
const ICLTensor *biases{nullptr};
ICLTensor *dst{nullptr};
std::unique_ptr<opencl::IClOperator> op{nullptr};
};
CLDeconvolutionLayer::~CLDeconvolutionLayer() = default;
CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_manager(std::move(memory_manager)), _function(), _impl(std::make_unique<Impl>())
{
}
void CLDeconvolutionLayer::configure(ICLTensor *input,
ICLTensor *weights,
const ICLTensor *bias,
ICLTensor *output,
const PadStrideInfo &deconv_info,
const WeightsInfo &weights_info)
{
configure(CLKernelLibrary::get().get_compile_context(), input, weights, bias, output, deconv_info, weights_info);
}
void CLDeconvolutionLayer::configure(const CLCompileContext &compile_context,
ICLTensor *input,
ICLTensor *weights,
const ICLTensor *bias,
ICLTensor *output,
const PadStrideInfo &deconv_info,
const WeightsInfo &weights_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_LOG_PARAMS(input, weights, bias, output, deconv_info, weights_info);
switch (CLDeconvolutionLayer::get_deconvolution_method(input->info(), weights->info(), nullptr, output->info(),
deconv_info, weights_info))
{
case DeconvolutionMethod::DIRECT:
{
auto op = std::make_unique<opencl::ClTransposedConvolution>();
op->configure(compile_context, input->info(), weights->info(), bias != nullptr ? bias->info() : nullptr,
output->info(), deconv_info);
_impl->src = input;
_impl->weights = weights;
_impl->biases = bias;
_impl->dst = output;
_impl->op = std::move(op);
break;
}
case DeconvolutionMethod::UPSCALE_CONV2D:
{
auto f = std::make_unique<CLDirectDeconvolutionLayer>();
f->configure(compile_context, input, weights, bias, output, deconv_info, weights_info);
_function = std::move(f);
break;
}
case DeconvolutionMethod::GEMM:
{
auto f = std::make_unique<CLGEMMDeconvolutionLayer>(_memory_manager);
f->configure(compile_context, input, weights, bias, output, deconv_info);
_function = std::move(f);
break;
}
default:
ARM_COMPUTE_ERROR("Not supported.");
break;
}
}
Status CLDeconvolutionLayer::validate(const ITensorInfo *input,
const ITensorInfo *weights,
const ITensorInfo *bias,
ITensorInfo *output,
const PadStrideInfo &deconv_info,
const WeightsInfo &weights_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
switch (CLDeconvolutionLayer::get_deconvolution_method(input, weights, bias, output, deconv_info, weights_info))
{
case DeconvolutionMethod::DIRECT:
{
// Validate transposed convolution operator
ARM_COMPUTE_RETURN_ON_ERROR(
opencl::ClTransposedConvolution::validate(input, weights, bias, output, deconv_info));
break;
}
case DeconvolutionMethod::UPSCALE_CONV2D:
{
// Validate direct convolution layer
ARM_COMPUTE_RETURN_ON_ERROR(
CLDirectDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, weights_info));
break;
}
case DeconvolutionMethod::GEMM:
{
// Validate gemm-based convolution layer
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMDeconvolutionLayer::validate(input, weights, bias, output, deconv_info));
break;
}
default:
ARM_COMPUTE_ERROR("Not supported.");
break;
}
return Status{};
}
DeconvolutionMethod CLDeconvolutionLayer::get_deconvolution_method(const ITensorInfo *input,
const ITensorInfo *weights,
const ITensorInfo *bias,
ITensorInfo *output,
const PadStrideInfo &deconv_info,
const WeightsInfo &weights_info)
{
ARM_COMPUTE_UNUSED(output, bias, weights_info);
if (is_data_type_quantized_per_channel(weights->data_type()))
{
return DeconvolutionMethod::UPSCALE_CONV2D;
}
const DataLayout data_layout = input->data_layout();
const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
const size_t idx_n = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
const size_t ofm = weights->tensor_shape()[idx_n];
if (weights->dimension(idx_w) != deconv_info.stride().first ||
weights->dimension(idx_h) != deconv_info.stride().second)
{
// We observe better performance for FP32 types only when ofm <= 16, and for FP16 only when ofm <= 32.
if (input->data_layout() == DataLayout::NHWC && !((input->data_type() == DataType::F32) && (ofm > 16)) &&
!((input->data_type() == DataType::F16) && (ofm > 32)))
{
return DeconvolutionMethod::DIRECT;
}
else
{
return DeconvolutionMethod::UPSCALE_CONV2D;
}
}
return DeconvolutionMethod::GEMM;
}
void CLDeconvolutionLayer::run()
{
prepare();
if (_impl->op != nullptr)
{
// Optimized Operator will be used
ITensorPack pack;
pack.add_tensor(TensorType::ACL_SRC_0, _impl->src);
pack.add_tensor(TensorType::ACL_SRC_1, _impl->weights);
pack.add_tensor(TensorType::ACL_SRC_2, _impl->biases);
pack.add_tensor(TensorType::ACL_DST, _impl->dst);
_impl->op->run(pack);
}
else
{
_function->run();
}
}
void CLDeconvolutionLayer::prepare()
{
if (_impl->op == nullptr)
{
_function->prepare();
}
}
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