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/*
* Copyright (c) 2020 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 "arm_compute/core/NEON/kernels/NEQLSTMLayerNormalizationKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/Tensor.h"
#include "arm_compute/runtime/TensorAllocator.h"
#include "tests/NEON/Accessor.h"
#include "tests/NEON/Helper.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/Validation.h"
#include "tests/validation/fixtures/QLSTMLayerNormalizationFixture.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
constexpr uint32_t vector_size_byte = 16;
using test::datasets::ShapeDataset;
using NEQLSTMLayerNormalization = NESynthetizeFunction<NEQLSTMLayerNormalizationKernel>;
template <uint32_t num_elements_per_iter, uint32_t num_batches, uint32_t num_iteration>
class QLSTMLayerNormShapeDataSet : public ShapeDataset
{
static constexpr auto boundary_minus_one = num_elements_per_iter * num_iteration - 1;
static constexpr auto boundary = num_elements_per_iter * num_iteration;
static constexpr auto boundary_plus_one = num_elements_per_iter * num_iteration + 1;
public:
QLSTMLayerNormShapeDataSet(std::string name)
: ShapeDataset(name,
{
TensorShape{ boundary_minus_one, num_batches },
TensorShape{ boundary, num_batches },
TensorShape{ boundary_plus_one, num_batches }
})
{
}
};
template <uint32_t num_elements_per_iter, uint32_t num_batches>
class QLSTMLayerNormShapeDataSet<num_elements_per_iter, num_batches, 0> : public ShapeDataset
{
public:
QLSTMLayerNormShapeDataSet(std::string name)
: ShapeDataset(name,
{
TensorShape{ 1, num_batches },
TensorShape{ 2, num_batches }
})
{
}
};
} // namespace
TEST_SUITE(NEON)
TEST_SUITE(QLSTMLayerNormalization)
static const TensorShape correct_input_shape{ TensorShape(15U, 2U) };
static const TensorShape correct_weight_shape{ TensorShape(15U) };
static const TensorShape correct_bias_shape{ TensorShape(15U) };
static const TensorShape correct_output_shape{ correct_input_shape };
static const DataType correct_input_dt{ DataType::QSYMM16 };
static const DataType correct_weight_dt{ DataType::QSYMM16 };
static const DataType correct_bias_dt{ DataType::S32 };
static const DataType correct_output_dt{ correct_input_dt };
static const uint32_t tensor_num_channel{ 1 };
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL,
zip(zip(zip(
framework::dataset::make("InputInfo", {
TensorInfo(correct_input_shape, tensor_num_channel, DataType::F16), // input supports only QSYMM16
TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // weight supports only QSYMM16
TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // bias supports only S32
TensorInfo(TensorShape(15U, 2U, 2U), tensor_num_channel, correct_input_dt), // input supports only up to 2D
TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // weight supports only up to 1D
TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // bias supports only up to 1D
TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // input_shape[0] != weight_shape[0] should fail
TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // weight_shape[0] != bias_shape[0] should fail
TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // output shape mismatches with input shape
TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // output data type mismatches with input data type
}),
framework::dataset::make("WeightInfo", {
TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
TensorInfo(correct_weight_shape, tensor_num_channel, DataType::F16),
TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
TensorInfo(TensorShape(15U, 2U), tensor_num_channel, correct_weight_dt),
TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
TensorInfo(TensorShape(14U), tensor_num_channel, correct_weight_dt),
TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
})
),
framework::dataset::make("BiasInfo", {
TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
TensorInfo(correct_bias_shape, tensor_num_channel, DataType::QSYMM16),
TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
TensorInfo(TensorShape(15U, 2U), tensor_num_channel, correct_bias_dt),
TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
TensorInfo(TensorShape(14U), tensor_num_channel, correct_bias_dt),
TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
})
),
framework::dataset::make("OutputInfo", {
TensorInfo(correct_output_shape, tensor_num_channel, correct_output_dt),
TensorInfo(correct_output_shape, tensor_num_channel, correct_output_dt),
TensorInfo(correct_output_shape, tensor_num_channel, correct_output_dt),
TensorInfo(correct_output_shape, tensor_num_channel, correct_output_dt),
TensorInfo(correct_output_shape, tensor_num_channel, correct_output_dt),
TensorInfo(correct_output_shape, tensor_num_channel, correct_output_dt),
TensorInfo(correct_output_shape, tensor_num_channel, correct_output_dt),
TensorInfo(correct_output_shape, tensor_num_channel, correct_output_dt),
TensorInfo(TensorShape(15, 3), tensor_num_channel, correct_output_dt),
TensorInfo(correct_output_shape, tensor_num_channel, DataType::S32),
})
),
input_info, weight_info, bias_info, output_info)
{
const Status s = NEQLSTMLayerNormalization::validate(&input_info, &output_info, &weight_info, &bias_info);
ARM_COMPUTE_EXPECT(!bool(s), framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
template <typename T>
using NEQLSTMLayerNormalizationFixture = QLSTMLayerNormalizationValidationFixture<Tensor, Accessor, NEQLSTMLayerNormalization, T>;
TEST_SUITE(Quantized)
TEST_SUITE(QSYMM16)
/** Tests will be targetting
* - Comparison between NEON kernel and the exact same but scalar version of reference kernel
* - Input shapes of 1D and 2D with the first dimension covers boundary values of 128-bit vector size (0~3 iterations)
* - Weight and bias 1D shape that have same size as that of input shapes
* - Quantization scale is greater and smaller than one.
* - Input values will be noted in fixture.
*
* What we can't test
* - Since reference kernel uses the exact the same algorithm in the same quantized domain
* it is hard to fully test whether the algorithm accomplishes what it is supposed to.
* - The algorithm has been sensitive to quantization scale but it is hard to fully test
* the sensitivity due to aforementioned reason.
* - Again, it is hard to fully test corner values due to the exact same algorithm of the
* reference kernel and the NEON kernel.
*/
constexpr uint32_t qsymm16_per_vector = vector_size_byte / sizeof(int16_t);
#define QSYMM16_DATASET_ITER(num_input_batch, num_iter) \
combine(combine(zip(zip(QLSTMLayerNormShapeDataSet<qsymm16_per_vector, num_input_batch, num_iter>("InputShape"), \
QLSTMLayerNormShapeDataSet<qsymm16_per_vector, 1, num_iter>("WeightShape")), \
QLSTMLayerNormShapeDataSet<qsymm16_per_vector, 1, num_iter>("BiasShape")), \
framework::dataset::make("DataType", DataType::QSYMM16)), \
framework::dataset::make("WeightQuantizationInfo", { QuantizationInfo(1. / 8192), QuantizationInfo(8192) }))
#define QSYMM16_DATASET_1D \
concat(concat(QSYMM16_DATASET_ITER(1, 0), QSYMM16_DATASET_ITER(1, 1)), QSYMM16_DATASET_ITER(1, 2))
#define QSYMM16_DATASET_2D \
concat(concat(QSYMM16_DATASET_ITER(3, 0), QSYMM16_DATASET_ITER(3, 1)), QSYMM16_DATASET_ITER(3, 2))
FIXTURE_DATA_TEST_CASE(RandomValue1D, NEQLSTMLayerNormalizationFixture<int16_t>, framework::DatasetMode::ALL, QSYMM16_DATASET_1D)
{
// Validate output
validate(Accessor(_target), _reference);
}
FIXTURE_DATA_TEST_CASE(RandomValue2D, NEQLSTMLayerNormalizationFixture<int16_t>, framework::DatasetMode::ALL, QSYMM16_DATASET_2D)
{
// Validate output
validate(Accessor(_target), _reference);
}
#undef QSYMM16_DATASET_ITER
#undef QSYMM16_DATASET_2D
#undef QSYMM16_DATASET_1D
TEST_SUITE_END() // QSYMM16
TEST_SUITE_END() // Quantized
TEST_SUITE_END() // QLSTMLayerNormalization
TEST_SUITE_END() // NEON
} // namespace validation
} // namespace test
} // namespace arm_compute
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