diff options
Diffstat (limited to 'tests/validation/NEON/GEMMLowp.cpp')
| -rw-r--r-- | tests/validation/NEON/GEMMLowp.cpp | 101 |
1 files changed, 101 insertions, 0 deletions
diff --git a/tests/validation/NEON/GEMMLowp.cpp b/tests/validation/NEON/GEMMLowp.cpp index 5d87330982..1941586d5c 100644 --- a/tests/validation/NEON/GEMMLowp.cpp +++ b/tests/validation/NEON/GEMMLowp.cpp @@ -26,6 +26,8 @@ #include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/runtime/TensorAllocator.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h" #include "tests/NEON/Accessor.h" #include "tests/NEON/Helper.h" #include "tests/PaddingCalculator.h" @@ -109,6 +111,105 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( // clang-format on // *INDENT-ON* +/** Test case for memory injection in @ref cpu::CpuGemmLowpMatrixMultiplyCore. + * + * Configure the operator once and inject memory at run-time in multiple executions. + * + * Checks performed in order: + * - Both runs compute the same output + */ +TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) +{ + auto gemm = std::make_unique<cpu::CpuGemmLowpMatrixMultiplyCore>(); + auto a_info = TensorInfo(TensorShape(32U, 72U), 1, DataType::QASYMM8); + auto b_info = TensorInfo(TensorShape(17U, 32U), 1, DataType::QASYMM8); + auto dst_info = TensorInfo(TensorShape(17U, 72U), 1, DataType::S32); + a_info.set_quantization_info(QuantizationInfo(1.0f / 255, -9)); + b_info.set_quantization_info(QuantizationInfo(1.0f / 255, 1)); + const auto gemm_info = GEMMInfo{}; + gemm->configure(&a_info, &b_info, nullptr, &dst_info, gemm_info); + + // telhs are newly created every call of this lambda function + auto a = create_tensor<Tensor>(a_info); + auto b = create_tensor<Tensor>(b_info); + auto dst = create_tensor<Tensor>(dst_info); + a.allocator()->allocate(); + b.allocator()->allocate(); + dst.allocator()->allocate(); + + ITensorPack run_pack = + { + { TensorType::ACL_SRC_0, &a }, + { TensorType::ACL_SRC_1, &b }, + { TensorType::ACL_DST, &dst } + }; + ITensorPack prep_pack = + { + { TensorType::ACL_SRC_1, &b }, + }; + + auto mg = MemoryGroup{}; + auto ws = manage_workspace<Tensor>(gemm->workspace(), mg, run_pack, prep_pack); + + auto run_conv = [&]() -> Tensor + { + auto dst = create_tensor<Tensor>(dst_info); + dst.allocator()->allocate(); + run_pack.add_tensor(TensorType::ACL_DST, &dst); + + library->fill_tensor_value(Accessor(a), static_cast<uint8_t>(1)); + library->fill_tensor_value(Accessor(b), static_cast<uint8_t>(2)); + // This operator is configured once and captured by this lambda. + gemm->prepare(prep_pack); + gemm->run(run_pack); + return dst; + }; + auto result_0 = run_conv(); + auto result_1 = run_conv(); + for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) + { + ARM_COMPUTE_EXPECT(((uint8_t *)result_0.buffer())[i] == ((uint8_t *)result_1.buffer())[i], framework::LogLevel::ERRORS); + } +} + +/** Test case for memory injection in @ref NEGEMMLowpMatrixMultiplyCore. + * + * Make sure @ref NEGEMMLowpMatrixMultiplyCore still works through injecting the memory at configure time using the old API. + * + * Checks performed in order: + * - Both runs compute the same output + */ +TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) +{ + auto gemm = std::make_unique<NEGEMMLowpMatrixMultiplyCore>(); + auto a_info = TensorInfo(TensorShape(32U, 72U), 1, DataType::QASYMM8); + auto b_info = TensorInfo(TensorShape(17U, 32U), 1, DataType::QASYMM8); + auto dst_info = TensorInfo(TensorShape(17U, 72U), 1, DataType::S32); + a_info.set_quantization_info(QuantizationInfo(1.0f / 255, -9)); + b_info.set_quantization_info(QuantizationInfo(1.0f / 255, 1)); + const auto gemm_info = GEMMInfo{}; + auto run_conv = [&]() + { + auto a = create_tensor<Tensor>(a_info); + auto b = create_tensor<Tensor>(b_info); + auto dst = create_tensor<Tensor>(dst_info); + gemm->configure(&a, &b, nullptr, &dst, gemm_info); + a.allocator()->allocate(); + b.allocator()->allocate(); + dst.allocator()->allocate(); + library->fill_tensor_value(Accessor(a), static_cast<uint8_t>(1)); + library->fill_tensor_value(Accessor(b), static_cast<uint8_t>(2)); + gemm->run(); + return dst; + }; + auto result_0 = run_conv(); + auto result_1 = run_conv(); + for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) + { + ARM_COMPUTE_EXPECT(((uint8_t *)result_0.buffer())[i] == ((uint8_t *)result_1.buffer())[i], framework::LogLevel::ERRORS); + } +} + FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMLowpMatrixMultiplyCoreFixture, framework::DatasetMode::ALL, datasets::SmallGEMMLowpDataset()) { // Validate output |