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/*
* Copyright (c) 2023-2024 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_DYNAMIC_FUSION_OPERATORS_MULFIXTURE_H
#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_MULFIXTURE_H
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
#include "tests/framework/Fixture.h"
#include "tests/framework/Macros.h"
#include "tests/Globals.h"
#include "tests/validation/reference/PixelWiseMultiplication.h"
using namespace arm_compute::experimental::dynamic_fusion;
namespace arm_compute
{
namespace test
{
namespace validation
{
/* We use a separate test fixture for Multiplication op instead of reusing ElementwiseBinaryFixture to avoid exposing
* the internal enum ElementwiseOp to the public utils/TypePrinters.h as required by the data test case macros
* to print the test data.
*/
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionMulValidationFixture : public framework::Fixture
{
public:
void setup(const TensorShape &shape0,
const TensorShape &shape1,
const TensorShape &shape2,
DataType data_type,
bool is_inplace,
bool fuse_two_ops = false)
{
_data_type = data_type;
_is_inplace = is_inplace;
_fuse = fuse_two_ops;
ARM_COMPUTE_ERROR_ON_MSG(_fuse && shape2.total_size() == 0, "No shape2 provided for fusion of two ops.");
ARM_COMPUTE_ERROR_ON_MSG(_fuse && _is_inplace, "In place for fusing case not supported yet.");
_target = compute_target(shape0, shape1, shape2);
_reference = compute_reference(shape0, shape1, shape2);
}
protected:
template <typename U>
void fill(U &&tensor, int i)
{
library->fill_tensor_uniform(tensor, i);
}
TensorType compute_target(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2)
{
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
auto context = GpuWorkloadContext{&cl_compile_ctx};
GpuWorkloadSketch sketch{&context};
// Fuse first multiplication op
ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(shape0, 1, _data_type));
ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(shape1, 1, _data_type));
ITensorInfo *dst_info = context.create_tensor_info();
ITensorInfo *rhs_info_fuse = nullptr;
ITensorInfo *ans_info = FunctionType::create_op(sketch, lhs_info, rhs_info);
if (_fuse)
{
rhs_info_fuse = context.create_tensor_info(TensorInfo(shape2, 1, _data_type));
ITensorInfo *ans2_info = FunctionType::create_op(sketch, ans_info, rhs_info_fuse);
GpuOutput::create_op(sketch, ans2_info, dst_info);
}
else
{
GpuOutput::create_op(sketch, ans_info, dst_info);
}
// Configure runtime
ClWorkloadRuntime runtime;
runtime.configure(sketch);
// (Important) Allocate auxiliary tensor memory if there are any
for (auto &data : runtime.get_auxiliary_tensors())
{
CLTensor *tensor = std::get<0>(data);
TensorInfo info = std::get<1>(data);
AuxMemoryInfo aux_mem_req = std::get<2>(data);
tensor->allocator()->init(info, aux_mem_req.alignment);
tensor->allocator()->allocate(); // Use ACL allocated memory
}
// Construct user tensors
TensorType t_lhs{};
TensorType t_rhs{};
TensorType t_rhs_fuse{};
TensorType t_dst{};
// Initialize user tensors
t_lhs.allocator()->init(*lhs_info);
t_rhs.allocator()->init(*rhs_info);
t_dst.allocator()->init(*dst_info);
if (_fuse)
{
t_rhs_fuse.allocator()->init(*rhs_info_fuse);
}
// Allocate and fill user tensors
// Instead of using ACL allocator, the user can choose to import memory into the tensors
t_lhs.allocator()->allocate();
t_rhs.allocator()->allocate();
t_dst.allocator()->allocate();
if (_fuse)
{
t_rhs_fuse.allocator()->allocate();
}
fill(AccessorType(t_lhs), 0);
fill(AccessorType(t_rhs), 1);
if (_fuse)
{
fill(AccessorType(t_rhs_fuse), 2);
}
// Run runtime
if (_fuse)
{
runtime.run({&t_lhs, &t_rhs, &t_rhs_fuse, &t_dst});
}
else
{
runtime.run({&t_lhs, &t_rhs, &t_dst});
}
return t_dst;
}
SimpleTensor<T> compute_reference(const TensorShape &shape0, const TensorShape &shape1, const TensorShape &shape2)
{
// Create reference
SimpleTensor<T> ref_lhs{shape0, _data_type, 1, QuantizationInfo()};
SimpleTensor<T> ref_rhs{shape1, _data_type, 1, QuantizationInfo()};
SimpleTensor<T> ref_rhs_fuse{shape2, _data_type, 1, QuantizationInfo()};
// Fill reference
fill(ref_lhs, 0);
fill(ref_rhs, 1);
SimpleTensor<T> ref_dst = reference::pixel_wise_multiplication<T, T, T>(
ref_lhs, ref_rhs, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_UP, _data_type,
QuantizationInfo());
if (_fuse)
{
fill(ref_rhs_fuse, 2);
SimpleTensor<T> ref_dst_fuse = reference::pixel_wise_multiplication<T, T, T>(
ref_dst, ref_rhs_fuse, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_UP, _data_type,
QuantizationInfo());
return ref_dst_fuse;
}
return ref_dst;
}
TensorType _target{};
SimpleTensor<T> _reference{};
DataType _data_type{};
bool _is_inplace{false};
bool _fuse{false};
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionMulOneOpValidationFixture
: public DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(const TensorShape &shape0, DataType data_type, bool is_inplace)
{
DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
shape0, shape0, TensorShape(), data_type, is_inplace);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionMulBroadcastValidationFixture
: public DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(const TensorShape &shape0, const TensorShape &shape1, DataType data_type, bool is_inplace)
{
DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
shape0, shape1, TensorShape(), data_type, is_inplace);
}
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class DynamicFusionMulTwoOpsValidationFixture
: public DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>
{
public:
void setup(const TensorShape &shape0,
const TensorShape &shape1,
const TensorShape &shape2,
DataType data_type,
bool is_inplace,
bool fuse_two_ops)
{
DynamicFusionMulValidationFixture<TensorType, AccessorType, FunctionType, T>::setup(
shape0, shape1, shape2, data_type, is_inplace, fuse_two_ops);
}
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_MULFIXTURE_H
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