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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_VALIDATION_FIXTURES_SCATTERLAYERFIXTURE_H
#define ACL_TESTS_VALIDATION_FIXTURES_SCATTERLAYERFIXTURE_H
#include "arm_compute/core/Utils.h"
#include "arm_compute/runtime/CL/CLTensorAllocator.h"
#include "tests/Globals.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/Validation.h"
#include "tests/validation/reference/ScatterLayer.h"
#include "tests/SimpleTensor.h"
#include <random>
#include <cstdint>
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ScatterGenericValidationFixture : public framework::Fixture
{
public:
void setup(TensorShape src_shape, TensorShape updates_shape, TensorShape indices_shape, TensorShape out_shape, DataType data_type, ScatterInfo scatter_info, QuantizationInfo src_qinfo = QuantizationInfo(), QuantizationInfo o_qinfo = QuantizationInfo())
{
_target = compute_target(src_shape, updates_shape, indices_shape, out_shape, data_type, scatter_info, src_qinfo, o_qinfo);
_reference = compute_reference(src_shape, updates_shape, indices_shape, out_shape, data_type,scatter_info, src_qinfo , o_qinfo);
}
protected:
template <typename U>
void fill(U &&tensor, int i, float lo = -10.f, float hi = 10.f)
{
switch(tensor.data_type())
{
case DataType::F32:
{
std::uniform_real_distribution<float> distribution(lo, hi);
library->fill(tensor, distribution, i);
break;
}
default:
{
ARM_COMPUTE_ERROR("Unsupported data type.");
}
}
}
// This is used to fill indices tensor with S32 datatype.
// Used to prevent ONLY having values that are out of bounds.
template <typename U>
void fill_indices(U &&tensor, int i, const TensorShape &shape)
{
// Calculate max indices the shape should contain. Add an arbitrary value to allow testing for some out of bounds values (In this case min dimension)
const int32_t max = std::max({shape[0] , shape[1], shape[2]});
library->fill_tensor_uniform(tensor, i, static_cast<int32_t>(-2), static_cast<int32_t>(max));
}
TensorType compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &shape_c, const TensorShape &out_shape, DataType data_type, const ScatterInfo info, QuantizationInfo a_qinfo, QuantizationInfo o_qinfo)
{
// 1. Create relevant tensors using ScatterInfo data structure.
// ----------------------------------------------------
// In order - src, updates, indices, output.
TensorType src = create_tensor<TensorType>(shape_a, data_type, 1, a_qinfo);
TensorType updates = create_tensor<TensorType>(shape_b, data_type, 1, a_qinfo);
TensorType indices = create_tensor<TensorType>(shape_c, DataType::S32, 1, QuantizationInfo());
TensorType dst = create_tensor<TensorType>(out_shape, data_type, 1, o_qinfo);
FunctionType scatter;
// Configure operator
// When scatter_info.zero_initialization is true, pass nullptr to scatter function.
if(info.zero_initialization)
{
scatter.configure(nullptr, &updates, &indices, &dst, info);
}
else
{
scatter.configure(&src, &updates, &indices, &dst, info);
}
// Assertions
ARM_COMPUTE_ASSERT(src.info()->is_resizable());
ARM_COMPUTE_ASSERT(updates.info()->is_resizable());
ARM_COMPUTE_ASSERT(indices.info()->is_resizable());
ARM_COMPUTE_ASSERT(dst.info()->is_resizable());
// Allocate tensors
src.allocator()->allocate();
updates.allocator()->allocate();
indices.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_ASSERT(!src.info()->is_resizable());
ARM_COMPUTE_ASSERT(!updates.info()->is_resizable());
ARM_COMPUTE_ASSERT(!indices.info()->is_resizable());
ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());
// Fill update (a) and indices (b) tensors.
fill(AccessorType(src), 0);
fill(AccessorType(updates), 1);
fill_indices(AccessorType(indices), 2, out_shape);
scatter.run();
return dst;
}
SimpleTensor<T> compute_reference(const TensorShape &a_shape, const TensorShape &b_shape, const TensorShape &c_shape, const TensorShape &out_shape, DataType data_type,
ScatterInfo info, QuantizationInfo a_qinfo, QuantizationInfo o_qinfo)
{
// Output Quantization not currently in use - fixture should be extended to support this.
ARM_COMPUTE_UNUSED(o_qinfo);
TensorShape src_shape = a_shape;
TensorShape updates_shape = b_shape;
TensorShape indices_shape = c_shape;
// 1. Collapse batch index into a single dim if necessary for update tensor and indices tensor.
if(c_shape.num_dimensions() >= 3)
{
indices_shape = indices_shape.collapsed_from(1);
updates_shape = updates_shape.collapsed_from(updates_shape.num_dimensions() - 2); // Collapses from last 2 dims
}
// 2. Collapse data dims into a single dim.
// Collapse all src dims into 2 dims. First one holding data, the other being the index we iterate over.
src_shape.collapse(updates_shape.num_dimensions() - 1); // Collapse all data dims into single dim.
src_shape = src_shape.collapsed_from(1); // Collapse all index dims into a single dim
updates_shape.collapse(updates_shape.num_dimensions() - 1); // Collapse data dims (all except last dim which is batch dim)
// Create reference tensors
SimpleTensor<T> src{ a_shape, data_type, 1, a_qinfo };
SimpleTensor<T> updates{b_shape, data_type, 1, QuantizationInfo() };
SimpleTensor<int32_t> indices{ c_shape, DataType::S32, 1, QuantizationInfo() };
// Fill reference
fill(src, 0);
fill(updates, 1);
fill_indices(indices, 2, out_shape);
// Calculate individual reference.
return reference::scatter_layer<T>(src, updates, indices, out_shape, info);
}
TensorType _target{};
SimpleTensor<T> _reference{};
};
// This fixture will use the same shape for updates as indices.
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ScatterValidationFixture : public ScatterGenericValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(TensorShape src_shape, TensorShape update_shape, TensorShape indices_shape, TensorShape out_shape, DataType data_type, ScatterFunction func, bool zero_init)
{
ScatterGenericValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(src_shape, update_shape, indices_shape, out_shape, data_type, ScatterInfo(func, zero_init), QuantizationInfo(), QuantizationInfo());
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif // ACL_TESTS_VALIDATION_FIXTURES_SCATTERLAYERFIXTURE_H
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