tests/validation/NEON/QLSTMLayerNormalization.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2020-2021 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/Types.h"
 #include "arm_compute/runtime/Tensor.h"
 #include "arm_compute/runtime/TensorAllocator.h"
 #include "src/core/NEON/kernels/NEQLSTMLayerNormalizationKernel.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 optimized 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 optimized 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
	/*
	* Copyright (c) 2020-2021 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/Types.h"
	#include "arm_compute/runtime/Tensor.h"
	#include "arm_compute/runtime/TensorAllocator.h"
	#include "src/core/NEON/kernels/NEQLSTMLayerNormalizationKernel.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 optimized 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 optimized 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