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/*
* Copyright (c) 2022-2024 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 "ClComponentDirectConv2d.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/dynamic_fusion/sketch/attributes/Conv2dAttributes.h"
#include "src/core/CL/CLValidate.h"
#include "src/dynamic_fusion/sketch/gpu/ckw_driver/components/GpuCkwDirectConv2d.h"
namespace arm_compute
{
namespace experimental
{
namespace dynamic_fusion
{
bool ClComponentDirectConv2dSettings::export_to_cl_image() const
{
return _desc.export_weights_to_cl_image;
}
ClComponentDirectConv2dSettings &ClComponentDirectConv2dSettings::fast_relaxed_math(bool fast_relaxed_math)
{
_fast_relaxed_math = fast_relaxed_math;
return *this;
}
bool ClComponentDirectConv2dSettings::fast_relaxed_math() const
{
return _fast_relaxed_math;
}
ClComponentDirectConv2dSettings &
ClComponentDirectConv2dSettings::direct_conv_descriptor(const DirectConvComputeKernelInfo &desc)
{
_desc = desc;
return *this;
}
DirectConvComputeKernelInfo ClComponentDirectConv2dSettings::direct_conv_descriptor() const
{
return _desc;
}
Status ClComponentDirectConv2d::validate(const Properties &properties,
const ArgumentPack<ITensorInfo> &tensors,
const Attributes &attributes,
const Settings &settings)
{
ARM_COMPUTE_UNUSED(properties);
const auto src = tensors.get_const_tensor(TensorType::ACL_SRC_0);
const auto wei = tensors.get_const_tensor(TensorType::ACL_SRC_1);
const auto bia = tensors.get_const_tensor(TensorType::ACL_SRC_2);
const auto dst = tensors.get_const_tensor(TensorType::ACL_DST_0);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, wei, dst);
// 1. Check validity
// Matching data type
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, wei);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
if (bia != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, bia);
}
// Matching data layout
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, wei);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, dst);
if (bia != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, bia);
}
// All tensor infos are initialized
ARM_COMPUTE_RETURN_ERROR_ON(src->tensor_shape().total_size() == 0);
ARM_COMPUTE_RETURN_ERROR_ON(wei->tensor_shape().total_size() == 0);
ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);
if (bia != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON(bia->tensor_shape().total_size() == 0);
}
// Device requirements are met
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src);
// wei shape is correct
const DataLayout data_layout = src->data_layout();
const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(wei->dimension(channel_idx) != src->dimension(channel_idx),
"Weights feature map dimension should match the respective src's one");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(wei->num_dimensions() > 4, "Weights can be at most 4 dimensional");
// dst shape is correct
PadStrideInfo legacy_pad_stride(attributes.stride().x(), attributes.stride().y(), attributes.pad().left,
attributes.pad().right, attributes.pad().top, attributes.pad().bottom,
DimensionRoundingType{});
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(
dst->tensor_shape(), misc::shape_calculator::compute_deep_convolution_shape(*src, *wei, legacy_pad_stride));
// bia shape is correct
if (bia != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(bia->dimension(0) != wei->dimension(3),
"Biases size and number of dst feature maps should match");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(bia->num_dimensions() > 1, "Biases should be one dimensional");
}
// 2. Check support level
// Data type
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
// Data layout
ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(src, DataLayout::NHWC);
const auto desc = settings.direct_conv_descriptor();
ARM_COMPUTE_RETURN_ERROR_ON_MSG(desc.n0 != 1 && desc.n0 != 2 && desc.n0 != 3 && desc.n0 != 4 && desc.n0 != 8 &&
desc.n0 != 16,
"N0 can only be: 1, 2, 3, 4, 8, and 16");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(desc.k0 != 1 && desc.k0 != 2 && desc.k0 != 3 && desc.k0 != 4 && desc.k0 != 8 &&
desc.k0 != 16,
"K0 can only be: 1, 2, 3, 4, 8, and 16");
return Status{};
}
ClComponentDirectConv2d::ClComponentDirectConv2d(ComponentId id,
const Properties &properties,
const ArgumentPack<ITensorInfo> &tensors,
const Attributes &attributes,
const Settings &settings)
: IGpuKernelComponent{id, properties, tensors},
_component_writer{std::make_unique<GpuCkwDirectConv2d>(id, tensors, attributes, settings)}
{
}
ClComponentDirectConv2d::~ClComponentDirectConv2d()
{
}
const IGpuCkwComponentDriver *ClComponentDirectConv2d::ckw_component_driver() const
{
return _component_writer.get();
}
} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute
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