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/*
* Copyright (c) 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.
*/
#ifndef ACL_TESTS_DATASETS_SCATTERDATASET_H
#define ACL_TESTS_DATASETS_SCATTERDATASET_H
#include "arm_compute/core/TensorShape.h"
#include "utils/TypePrinter.h"
namespace arm_compute
{
namespace test
{
namespace datasets
{
class ScatterDataset
{
public:
using type = std::tuple<TensorShape, TensorShape, TensorShape, TensorShape>;
struct iterator
{
iterator(std::vector<TensorShape>::const_iterator src_it,
std::vector<TensorShape>::const_iterator updates_it,
std::vector<TensorShape>::const_iterator indices_it,
std::vector<TensorShape>::const_iterator dst_it)
: _src_it{ std::move(src_it) },
_updates_it{ std::move(updates_it) },
_indices_it{std::move(indices_it)},
_dst_it{ std::move(dst_it) }
{
}
std::string description() const
{
std::stringstream description;
description << "A=" << *_src_it << ":";
description << "B=" << *_updates_it << ":";
description << "C=" << *_indices_it << ":";
description << "Out=" << *_dst_it << ":";
return description.str();
}
ScatterDataset::type operator*() const
{
return std::make_tuple(*_src_it, *_updates_it, *_indices_it, *_dst_it);
}
iterator &operator++()
{
++_src_it;
++_updates_it;
++_indices_it;
++_dst_it;
return *this;
}
private:
std::vector<TensorShape>::const_iterator _src_it;
std::vector<TensorShape>::const_iterator _updates_it;
std::vector<TensorShape>::const_iterator _indices_it;
std::vector<TensorShape>::const_iterator _dst_it;
};
iterator begin() const
{
return iterator(_src_shapes.begin(), _update_shapes.begin(), _indices_shapes.begin(), _dst_shapes.begin());
}
int size() const
{
return std::min(_src_shapes.size(), std::min(_indices_shapes.size(), std::min(_update_shapes.size(), _dst_shapes.size())));
}
void add_config(TensorShape a, TensorShape b, TensorShape c, TensorShape dst)
{
_src_shapes.emplace_back(std::move(a));
_update_shapes.emplace_back(std::move(b));
_indices_shapes.emplace_back(std::move(c));
_dst_shapes.emplace_back(std::move(dst));
}
protected:
ScatterDataset() = default;
ScatterDataset(ScatterDataset &&) = default;
private:
std::vector<TensorShape> _src_shapes{};
std::vector<TensorShape> _update_shapes{};
std::vector<TensorShape> _indices_shapes{};
std::vector<TensorShape> _dst_shapes{};
};
// 1D dataset for simple scatter tests.
class Small1DScatterDataset final : public ScatterDataset
{
public:
Small1DScatterDataset()
{
add_config(TensorShape(6U), TensorShape(6U), TensorShape(1U, 6U), TensorShape(6U));
add_config(TensorShape(10U), TensorShape(2U), TensorShape(1U, 2U), TensorShape(10U));
}
};
// This dataset represents the (m+1)-D updates/dst case.
class SmallScatterMultiDimDataset final : public ScatterDataset
{
public:
SmallScatterMultiDimDataset()
{
// NOTE: Config is src, updates, indices, output.
// - In this config, the dim replaced is the final number (largest tensor dimension)
// - Largest "updates" dim should match y-dim of indices.
// - src/updates/dst should all have same number of dims. Indices should be 2D.
add_config(TensorShape(6U, 5U), TensorShape(6U, 2U), TensorShape(1U, 2U), TensorShape(6U, 5U));
add_config(TensorShape(9U, 3U, 4U), TensorShape(9U, 3U, 2U), TensorShape(1U, 2U), TensorShape(9U, 3U, 4U));
add_config(TensorShape(3U, 2U, 4U, 2U), TensorShape(3U, 2U, 4U, 2U), TensorShape(1U, 2U), TensorShape(3U, 2U, 4U, 2U));
}
};
// This dataset represents the (m+1)-D updates tensor, (m+n)-d output tensor cases
class SmallScatterMultiIndicesDataset final : public ScatterDataset
{
public:
SmallScatterMultiIndicesDataset()
{
// NOTE: Config is src, updates, indices, output.
// NOTE: indices.shape.x = src.num_dimensions - updates.num_dimensions + 1
// index length is 2
add_config(TensorShape(6U, 5U, 2U), TensorShape(6U, 4U), TensorShape(2U, 4U), TensorShape(6U, 5U, 2U));
add_config(TensorShape(17U, 3U, 3U, 2U), TensorShape(17U, 3U, 2U), TensorShape(2U, 2U), TensorShape(17U, 3U, 3U, 2U));
add_config(TensorShape(11U, 3U, 3U, 2U, 4U), TensorShape(11U, 3U, 3U, 4U), TensorShape(2U, 4U), TensorShape(11U, 3U, 3U, 2U, 4U));
add_config(TensorShape(5U, 4U, 3U, 3U, 2U, 4U), TensorShape(5U, 4U, 3U, 3U, 5U), TensorShape(2U, 5U), TensorShape(5U, 4U, 3U, 3U, 2U, 4U));
// index length is 3
add_config(TensorShape(4U, 3U, 2U, 2U), TensorShape(4U, 2U), TensorShape(3U, 2U), TensorShape(4U, 3U, 2U, 2U));
add_config(TensorShape(17U, 4U, 3U, 2U, 2U), TensorShape(17U, 4U, 4U), TensorShape(3U, 4U), TensorShape(17U, 4U, 3U, 2U, 2U));
add_config(TensorShape(10U, 4U, 5U, 3U, 2U, 2U), TensorShape(10U, 4U, 5U, 3U), TensorShape(3U, 3U), TensorShape(10U, 4U, 5U, 3U, 2U, 2U));
// index length is 4
add_config(TensorShape(35U, 4U, 3U, 2U, 2U), TensorShape(35U, 4U), TensorShape(4U, 4U), TensorShape(35U, 4U, 3U, 2U, 2U));
add_config(TensorShape(10U, 4U, 5U, 3U, 2U, 2U), TensorShape(10U, 4U, 3U), TensorShape(4U, 3U), TensorShape(10U, 4U, 5U, 3U, 2U, 2U));
// index length is 5
add_config(TensorShape(10U, 4U, 5U, 3U, 2U, 2U), TensorShape(10U, 3U), TensorShape(5U, 3U), TensorShape(10U, 4U, 5U, 3U, 2U, 2U));
}
};
// This dataset represents the (m+k)-D updates tensor, (k+1)-d indices tensor and (m+n)-d output tensor cases
class SmallScatterBatchedDataset final : public ScatterDataset
{
public:
SmallScatterBatchedDataset()
{
// NOTE: Config is src, updates, indices, output.
// NOTE: Updates/Indices tensors are now batched.
// NOTE: indices.shape.x = (updates_batched) ? (src.num_dimensions - updates.num_dimensions) + 2 : (src.num_dimensions - updates.num_dimensions) + 1
add_config(TensorShape(6U, 5U), TensorShape(6U, 2U, 2U), TensorShape(1U, 2U, 2U), TensorShape(6U, 5U));
add_config(TensorShape(6U, 5U, 2U), TensorShape(6U, 2U, 2U), TensorShape(2U, 2U, 2U), TensorShape(6U, 5U, 2U));
add_config(TensorShape(6U, 5U, 2U, 2U), TensorShape(3U, 2U), TensorShape(4U, 3U, 2U), TensorShape(6U, 5U, 2U, 2U));
add_config(TensorShape(5U, 5U, 4U, 2U, 2U), TensorShape(6U, 2U), TensorShape(5U, 6U, 2U), TensorShape(5U, 5U, 4U, 2U, 2U));
}
};
} // namespace datasets
} // namespace test
} // namespace arm_compute
#endif // ACL_TESTS_DATASETS_SCATTERDATASET_H
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