| /* |
| * 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 "src/core/CL/kernels/CLQLSTMLayerNormalizationKernel.h" |
| #include "tests/CL/CLAccessor.h" |
| #include "tests/CL/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 AbsoluteTolerance<int16_t> tolerance_s16(0); /**< Tolerance value for comparing reference's output against implementation's output for QSYMM16 data types */ |
| constexpr uint32_t vector_size_byte = 16; |
| |
| using test::datasets::ShapeDataset; |
| using CLQLSTMLayerNormalization = CLSynthetizeFunction<CLQLSTMLayerNormalizationKernel>; |
| 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(CL) |
| 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 DataType correct_input_dt{ DataType::QSYMM16 }; |
| static const DataType correct_weight_dt{ DataType::QSYMM16 }; |
| static const DataType correct_bias_dt{ DataType::S32 }; |
| static const uint32_t tensor_num_channel{ 1 }; |
| |
| // *INDENT-OFF* |
| // clang-format off |
| |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, |
| 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 |
| }), |
| 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), |
| }) |
| ), |
| 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), |
| }) |
| ), input_info, weight_info, bias_info) |
| { |
| TensorInfo dummy_output{}; |
| const Status s = CLQLSTMLayerNormalization::validate(&input_info, &dummy_output, &weight_info, &bias_info); |
| ARM_COMPUTE_EXPECT(!bool(s), framework::LogLevel::ERRORS); |
| } |
| |
| // clang-format on |
| // *INDENT-ON* |
| |
| template <typename T> |
| using CLQLSTMLayerNormalizationFixture = QLSTMLayerNormalizationValidationFixture<CLTensor, CLAccessor, CLQLSTMLayerNormalization, T>; |
| |
| TEST_SUITE(Quantized) |
| TEST_SUITE(QSYMM16) |
| |
| /** Tests will be targetting |
| * - Comparison between OpenCL 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 OpenCL 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("InputQuantizationInfo", { 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, CLQLSTMLayerNormalizationFixture<int16_t>, framework::DatasetMode::ALL, QSYMM16_DATASET_1D) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_s16); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RandomValue2D, CLQLSTMLayerNormalizationFixture<int16_t>, framework::DatasetMode::ALL, QSYMM16_DATASET_2D) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_s16); |
| } |
| |
| #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() // CL |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |