/* * 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 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; template 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 class QLSTMLayerNormShapeDataSet : 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 using CLQLSTMLayerNormalizationFixture = QLSTMLayerNormalizationValidationFixture; 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("InputShape"), \ QLSTMLayerNormShapeDataSet("WeightShape")), \ QLSTMLayerNormShapeDataSet("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, framework::DatasetMode::ALL, QSYMM16_DATASET_1D) { // Validate output validate(CLAccessor(_target), _reference, tolerance_s16); } FIXTURE_DATA_TEST_CASE(RandomValue2D, CLQLSTMLayerNormalizationFixture, 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