200 lines
9.2 KiB
C++
200 lines
9.2 KiB
C++
/*
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* Copyright (c) 2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.h"
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#include "tests/CL/CLAccessor.h"
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#include "tests/CL/Helper.h"
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#include "tests/PaddingCalculator.h"
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#include "tests/datasets/ShapeDatasets.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Macros.h"
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#include "tests/framework/datasets/Datasets.h"
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#include "tests/validation/Helpers.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/fixtures/QLSTMLayerNormalizationFixture.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace
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{
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constexpr AbsoluteTolerance<int16_t> tolerance_s16(0); /**< Tolerance value for comparing reference's output against implementation's output for QSYMM16 data types */
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constexpr uint32_t vector_size_byte = 16;
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using test::datasets::ShapeDataset;
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using CLQLSTMLayerNormalization = CLSynthetizeFunction<CLQLSTMLayerNormalizationKernel>;
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template <uint32_t num_elements_per_iter, uint32_t num_batches, uint32_t num_iteration>
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class QLSTMLayerNormShapeDataSet : public ShapeDataset
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{
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static constexpr auto boundary_minus_one = num_elements_per_iter * num_iteration - 1;
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static constexpr auto boundary = num_elements_per_iter * num_iteration;
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static constexpr auto boundary_plus_one = num_elements_per_iter * num_iteration + 1;
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public:
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QLSTMLayerNormShapeDataSet(std::string name)
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: ShapeDataset(name,
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{
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TensorShape{ boundary_minus_one, num_batches },
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TensorShape{ boundary, num_batches },
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TensorShape{ boundary_plus_one, num_batches }
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})
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{
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}
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};
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template <uint32_t num_elements_per_iter, uint32_t num_batches>
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class QLSTMLayerNormShapeDataSet<num_elements_per_iter, num_batches, 0> : public ShapeDataset
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{
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public:
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QLSTMLayerNormShapeDataSet(std::string name)
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: ShapeDataset(name,
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{
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TensorShape{ 1, num_batches },
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TensorShape{ 2, num_batches }
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})
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{
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}
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};
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} // namespace
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TEST_SUITE(CL)
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TEST_SUITE(QLSTMLayerNormalization)
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static const TensorShape correct_input_shape{ TensorShape(15U, 2U) };
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static const TensorShape correct_weight_shape{ TensorShape(15U) };
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static const TensorShape correct_bias_shape{ TensorShape(15U) };
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static const DataType correct_input_dt{ DataType::QSYMM16 };
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static const DataType correct_weight_dt{ DataType::QSYMM16 };
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static const DataType correct_bias_dt{ DataType::S32 };
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static const uint32_t tensor_num_channel{ 1 };
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// *INDENT-OFF*
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// clang-format off
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DATA_TEST_CASE(Validate, framework::DatasetMode::ALL,
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zip(zip(
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framework::dataset::make("InputInfo", {
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TensorInfo(correct_input_shape, tensor_num_channel, DataType::F16), // input supports only QSYMM16
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TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // weight supports only QSYMM16
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TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // bias supports only S32
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TensorInfo(TensorShape(15U, 2U, 2U), tensor_num_channel, correct_input_dt), // input supports only up to 2D
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TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // weight supports only up to 1D
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TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // bias supports only up to 1D
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TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // input_shape[0] != weight_shape[0] should fail
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TensorInfo(correct_input_shape, tensor_num_channel, correct_input_dt), // weight_shape[0] != bias_shape[0] should fail
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}),
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framework::dataset::make("WeightInfo", {
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TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
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TensorInfo(correct_weight_shape, tensor_num_channel, DataType::F16),
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TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
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TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
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TensorInfo(TensorShape(15U, 2U), tensor_num_channel, correct_weight_dt),
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TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
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TensorInfo(TensorShape(14U), tensor_num_channel, correct_weight_dt),
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TensorInfo(correct_weight_shape, tensor_num_channel, correct_weight_dt),
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})
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),
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framework::dataset::make("BiasInfo", {
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TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
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TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
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TensorInfo(correct_bias_shape, tensor_num_channel, DataType::QSYMM16),
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TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
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TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
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TensorInfo(TensorShape(15U, 2U), tensor_num_channel, correct_bias_dt),
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TensorInfo(correct_bias_shape, tensor_num_channel, correct_bias_dt),
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TensorInfo(TensorShape(14U), tensor_num_channel, correct_bias_dt),
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})
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), input_info, weight_info, bias_info)
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{
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TensorInfo dummy_output{};
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const Status s = CLQLSTMLayerNormalization::validate(&input_info, &dummy_output, &weight_info, &bias_info);
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ARM_COMPUTE_EXPECT(!bool(s), framework::LogLevel::ERRORS);
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}
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// clang-format on
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// *INDENT-ON*
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template <typename T>
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using CLQLSTMLayerNormalizationFixture = QLSTMLayerNormalizationValidationFixture<CLTensor, CLAccessor, CLQLSTMLayerNormalization, T>;
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TEST_SUITE(Quantized)
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TEST_SUITE(QSYMM16)
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/** Tests will be targetting
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* - Comparison between OpenCL kernel and the exact same but scalar version of reference kernel
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* - Input shapes of 1D and 2D with the first dimension covers boundary values of 128-bit vector size (0~3 iterations)
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* - Weight and bias 1D shape that have same size as that of input shapes
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* - Quantization scale is greater and smaller than one.
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* - Input values will be noted in fixture.
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*
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* What we can't test
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* - Since reference kernel uses the exact the same algorithm in the same quantized domain
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* it is hard to fully test whether the algorithm accomplishes what it is supposed to.
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* - The algorithm has been sensitive to quantization scale but it is hard to fully test
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* the sensitivity due to aforementioned reason.
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* - Again, it is hard to fully test corner values due to the exact same algorithm of the
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* reference kernel and the OpenCL kernel.
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*/
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constexpr uint32_t qsymm16_per_vector = vector_size_byte / sizeof(int16_t);
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#define QSYMM16_DATASET_ITER(num_input_batch, num_iter) \
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combine(combine(zip(zip(QLSTMLayerNormShapeDataSet<qsymm16_per_vector, num_input_batch, num_iter>("InputShape"), \
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QLSTMLayerNormShapeDataSet<qsymm16_per_vector, 1, num_iter>("WeightShape")), \
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QLSTMLayerNormShapeDataSet<qsymm16_per_vector, 1, num_iter>("BiasShape")), \
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framework::dataset::make("DataType", DataType::QSYMM16)), \
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framework::dataset::make("InputQuantizationInfo", { QuantizationInfo(1. / 8192), QuantizationInfo(8192) }))
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#define QSYMM16_DATASET_1D \
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concat(concat(QSYMM16_DATASET_ITER(1, 0), QSYMM16_DATASET_ITER(1, 1)), QSYMM16_DATASET_ITER(1, 2))
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#define QSYMM16_DATASET_2D \
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concat(concat(QSYMM16_DATASET_ITER(3, 0), QSYMM16_DATASET_ITER(3, 1)), QSYMM16_DATASET_ITER(3, 2))
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FIXTURE_DATA_TEST_CASE(RandomValue1D, CLQLSTMLayerNormalizationFixture<int16_t>, framework::DatasetMode::ALL, QSYMM16_DATASET_1D)
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_s16);
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}
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FIXTURE_DATA_TEST_CASE(RandomValue2D, CLQLSTMLayerNormalizationFixture<int16_t>, framework::DatasetMode::ALL, QSYMM16_DATASET_2D)
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{
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// Validate output
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validate(CLAccessor(_target), _reference, tolerance_s16);
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}
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#undef QSYMM16_DATASET_ITER
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#undef QSYMM16_DATASET_2D
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#undef QSYMM16_DATASET_1D
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TEST_SUITE_END() // QSYMM16
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TEST_SUITE_END() // Quantized
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TEST_SUITE_END() // QLSTMLayerNormalization
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TEST_SUITE_END() // CL
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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