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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.
*/
#ifndef ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CASTFIXTURE_H
#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CASTFIXTURE_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/attributes/CastAttributes.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/validation/reference/DepthConvertLayer.h"
using namespace arm_compute::experimental::dynamic_fusion;
namespace arm_compute
{
namespace test
{
namespace validation
{
template <typename TensorType, typename AccessorType, typename FunctionType, typename T1, typename T2>
class DynamicFusionCastValidationFixture : public framework::Fixture
{
public:
void setup(TensorShape shape, DataType dt_in, DataType dt_out, ConvertPolicy policy)
{
_target = compute_target(shape, dt_in, dt_out, policy);
_reference = compute_reference(shape, dt_in, dt_out, policy);
}
protected:
template <typename U>
void fill(U &&tensor, int i, DataType dt_in, DataType dt_out)
{
// Restricting range to avoid inf values
if (dt_out == DataType::F16)
{
constexpr int signed_min = -32000;
constexpr int signed_max = 32000;
constexpr int unsigned_min = 0;
constexpr int unsigned_max = 65000;
switch (dt_in)
{
case DataType::U8:
case DataType::QASYMM8:
case DataType::QASYMM8_SIGNED:
case DataType::S8:
case DataType::F32:
{
library->fill_tensor_uniform(tensor, i);
break;
}
case DataType::U16:
{
library->fill_tensor_uniform(tensor, i, static_cast<uint16_t>(unsigned_min),
static_cast<uint16_t>(unsigned_max));
break;
}
case DataType::S16:
{
library->fill_tensor_uniform(tensor, i, static_cast<int16_t>(signed_min),
static_cast<int16_t>(signed_max));
break;
}
case DataType::U32:
{
library->fill_tensor_uniform(tensor, i, static_cast<uint32_t>(unsigned_min),
static_cast<uint32_t>(unsigned_max));
break;
}
case DataType::S32:
{
library->fill_tensor_uniform(tensor, i, static_cast<int32_t>(signed_min),
static_cast<int32_t>(signed_max));
break;
}
default:
ARM_COMPUTE_ERROR("NOT SUPPORTED!");
}
}
else
{
library->fill_tensor_uniform(tensor, i);
}
}
// Given input is in nchw format
TensorType
compute_target(const TensorShape &shape, const DataType dt_in, const DataType dt_out, const ConvertPolicy policy)
{
// Create a new workload sketch
auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
auto context = GpuWorkloadContext{&cl_compile_ctx};
GpuWorkloadSketch sketch{&context};
// Create sketch tensors
// Here, we use DataLayout::NCHW just for the test. However, the optimal data layout to
// be used with dynamic fusion is NHWC
ITensorInfo *src_info =
context.create_tensor_info(TensorInfo(shape, 1, dt_in, DataLayout::NCHW)); // layout is not important
ITensorInfo *dst_info = context.create_tensor_info();
CastAttributes attributes;
attributes.convert_policy(policy).data_type(dt_out);
ITensorInfo *ans_info = FunctionType::create_op(sketch, src_info, attributes);
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_src{};
TensorType t_dst{};
// Initialize user tensors
t_src.allocator()->init(*src_info);
t_dst.allocator()->init(*dst_info);
// Allocate and fill user tensors
t_src.allocator()->allocate();
t_dst.allocator()->allocate();
fill(AccessorType(t_src), 0, dt_in, dt_out);
// Run runtime
runtime.run({&t_src, &t_dst});
return t_dst;
}
SimpleTensor<T2>
compute_reference(const TensorShape &shape, const DataType dt_in, const DataType dt_out, const ConvertPolicy policy)
{
// Create reference
SimpleTensor<T1> src{shape, dt_in, 1};
// Fill reference
fill(src, 0, dt_in, dt_out);
return reference::depth_convert<T1, T2>(src, dt_out, policy, 0);
}
TensorType _target{};
SimpleTensor<T2> _reference{};
};
} // namespace validation
} // namespace test
} // namespace arm_compute
#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_OPERATORS_CASTFIXTURE_H
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