| /* |
| * Copyright (c) 2019-2021 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. |
| */ |
| #ifndef ARM_COMPUTE_TEST_UNIT_DYNAMIC_TENSOR |
| #define ARM_COMPUTE_TEST_UNIT_DYNAMIC_TENSOR |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "tests/AssetsLibrary.h" |
| #include "tests/Globals.h" |
| #include "tests/IAccessor.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Fixture.h" |
| #include "tests/validation/Helpers.h" |
| #include "tests/validation/reference/ConvolutionLayer.h" |
| #include "tests/validation/reference/NormalizationLayer.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| template <typename AllocatorType, |
| typename LifetimeMgrType, |
| typename PoolMgrType, |
| typename MemoryMgrType> |
| struct MemoryManagementService |
| { |
| public: |
| using LftMgrType = LifetimeMgrType; |
| |
| public: |
| MemoryManagementService() |
| : allocator(), lifetime_mgr(nullptr), pool_mgr(nullptr), mm(nullptr), mg(), num_pools(0) |
| { |
| lifetime_mgr = std::make_shared<LifetimeMgrType>(); |
| pool_mgr = std::make_shared<PoolMgrType>(); |
| mm = std::make_shared<MemoryMgrType>(lifetime_mgr, pool_mgr); |
| mg = MemoryGroup(mm); |
| } |
| |
| void populate(size_t pools) |
| { |
| mm->populate(allocator, pools); |
| num_pools = pools; |
| } |
| |
| void clear() |
| { |
| mm->clear(); |
| num_pools = 0; |
| } |
| |
| void validate(bool validate_finalized) const |
| { |
| ARM_COMPUTE_ASSERT(mm->pool_manager() != nullptr); |
| ARM_COMPUTE_ASSERT(mm->lifetime_manager() != nullptr); |
| |
| if(validate_finalized) |
| { |
| ARM_COMPUTE_ASSERT(mm->lifetime_manager()->are_all_finalized()); |
| } |
| ARM_COMPUTE_ASSERT(mm->pool_manager()->num_pools() == num_pools); |
| } |
| |
| AllocatorType allocator; |
| std::shared_ptr<LifetimeMgrType> lifetime_mgr; |
| std::shared_ptr<PoolMgrType> pool_mgr; |
| std::shared_ptr<MemoryMgrType> mm; |
| MemoryGroup mg; |
| size_t num_pools; |
| }; |
| |
| template <typename MemoryMgrType, typename FuncType, typename ITensorType> |
| class SimpleFunctionWrapper |
| { |
| public: |
| SimpleFunctionWrapper(std::shared_ptr<MemoryMgrType> mm) |
| : _func(mm) |
| { |
| } |
| void configure(ITensorType *src, ITensorType *dst) |
| { |
| ARM_COMPUTE_UNUSED(src, dst); |
| } |
| void run() |
| { |
| _func.run(); |
| } |
| |
| private: |
| FuncType _func; |
| }; |
| |
| /** Simple test case to run a single function with different shapes twice. |
| * |
| * Runs a specified function twice, where the second time the size of the input/output is different |
| * Internal memory of the function and input/output are managed by different services |
| */ |
| template <typename TensorType, |
| typename AccessorType, |
| typename MemoryManagementServiceType, |
| typename SimpleFunctionWrapperType> |
| class DynamicTensorType3SingleFunction : public framework::Fixture |
| { |
| using T = float; |
| |
| public: |
| template <typename...> |
| void setup(TensorShape input_level0, TensorShape input_level1) |
| { |
| input_l0 = input_level0; |
| input_l1 = input_level1; |
| run(); |
| } |
| |
| protected: |
| void run() |
| { |
| MemoryManagementServiceType serv_internal; |
| MemoryManagementServiceType serv_cross; |
| const size_t num_pools = 1; |
| const bool validate_finalized = true; |
| |
| // Create Tensor shapes. |
| TensorShape level_0 = TensorShape(input_l0); |
| TensorShape level_1 = TensorShape(input_l1); |
| |
| // Level 0 |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(level_0, DataType::F32, 1); |
| TensorType dst = create_tensor<TensorType>(level_0, DataType::F32, 1); |
| |
| serv_cross.mg.manage(&src); |
| serv_cross.mg.manage(&dst); |
| |
| // Create and configure function |
| SimpleFunctionWrapperType layer(serv_internal.mm); |
| layer.configure(&src, &dst); |
| |
| ARM_COMPUTE_ASSERT(src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); |
| |
| // Populate and validate memory manager |
| serv_cross.populate(num_pools); |
| serv_internal.populate(num_pools); |
| serv_cross.validate(validate_finalized); |
| serv_internal.validate(validate_finalized); |
| |
| // Extract lifetime manager meta-data information |
| internal_l0 = serv_internal.lifetime_mgr->info(); |
| cross_l0 = serv_cross.lifetime_mgr->info(); |
| |
| // Acquire memory manager, fill tensors and compute functions |
| serv_cross.mg.acquire(); |
| arm_compute::test::library->fill_tensor_value(AccessorType(src), 12.f); |
| layer.run(); |
| serv_cross.mg.release(); |
| |
| // Clear manager |
| serv_cross.clear(); |
| serv_internal.clear(); |
| serv_cross.validate(validate_finalized); |
| serv_internal.validate(validate_finalized); |
| |
| // Level 1 |
| // Update the tensor shapes |
| src.info()->set_tensor_shape(level_1); |
| dst.info()->set_tensor_shape(level_1); |
| src.info()->set_is_resizable(true); |
| dst.info()->set_is_resizable(true); |
| |
| serv_cross.mg.manage(&src); |
| serv_cross.mg.manage(&dst); |
| |
| // Re-configure the function |
| layer.configure(&src, &dst); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| // Populate and validate memory manager |
| serv_cross.populate(num_pools); |
| serv_internal.populate(num_pools); |
| serv_cross.validate(validate_finalized); |
| serv_internal.validate(validate_finalized); |
| |
| // Extract lifetime manager meta-data information |
| internal_l1 = serv_internal.lifetime_mgr->info(); |
| cross_l1 = serv_cross.lifetime_mgr->info(); |
| |
| // Compute functions |
| serv_cross.mg.acquire(); |
| arm_compute::test::library->fill_tensor_value(AccessorType(src), 12.f); |
| layer.run(); |
| serv_cross.mg.release(); |
| |
| // Clear manager |
| serv_cross.clear(); |
| serv_internal.clear(); |
| serv_cross.validate(validate_finalized); |
| serv_internal.validate(validate_finalized); |
| } |
| |
| public: |
| TensorShape input_l0{}, input_l1{}; |
| typename MemoryManagementServiceType::LftMgrType::info_type internal_l0{}, internal_l1{}; |
| typename MemoryManagementServiceType::LftMgrType::info_type cross_l0{}, cross_l1{}; |
| }; |
| |
| /** Simple test case to run a single function with different shapes twice. |
| * |
| * Runs a specified function twice, where the second time the size of the input/output is different |
| * Internal memory of the function and input/output are managed by different services |
| */ |
| template <typename TensorType, |
| typename AccessorType, |
| typename MemoryManagementServiceType, |
| typename ComplexFunctionType> |
| class DynamicTensorType3ComplexFunction : public framework::Fixture |
| { |
| using T = float; |
| |
| public: |
| template <typename...> |
| void setup(std::vector<TensorShape> input_shapes, TensorShape weights_shape, TensorShape bias_shape, std::vector<TensorShape> output_shapes, PadStrideInfo info) |
| { |
| num_iterations = input_shapes.size(); |
| _data_type = DataType::F32; |
| _data_layout = DataLayout::NHWC; |
| _input_shapes = input_shapes; |
| _output_shapes = output_shapes; |
| _weights_shape = weights_shape; |
| _bias_shape = bias_shape; |
| _info = info; |
| |
| // Create function |
| _f_target = std::make_unique<ComplexFunctionType>(_ms.mm); |
| } |
| |
| void run_iteration(unsigned int idx) |
| { |
| auto input_shape = _input_shapes[idx]; |
| auto output_shape = _output_shapes[idx]; |
| |
| dst_ref = run_reference(input_shape, _weights_shape, _bias_shape, output_shape, _info); |
| dst_target = run_target(input_shape, _weights_shape, _bias_shape, output_shape, _info, WeightsInfo()); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::F32: |
| { |
| std::uniform_real_distribution<> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| default: |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| TensorType run_target(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, |
| PadStrideInfo info, WeightsInfo weights_info) |
| { |
| if(_data_layout == DataLayout::NHWC) |
| { |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| permute(output_shape, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| _weights_target = create_tensor<TensorType>(weights_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| _bias_target = create_tensor<TensorType>(bias_shape, _data_type, 1); |
| |
| // Create tensors |
| TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, QuantizationInfo(), _data_layout); |
| |
| // Create and configure function |
| _f_target->configure(&src, &_weights_target, &_bias_target, &dst, info, weights_info); |
| |
| ARM_COMPUTE_ASSERT(src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| _weights_target.allocator()->allocate(); |
| _bias_target.allocator()->allocate(); |
| |
| ARM_COMPUTE_ASSERT(!src.info()->is_resizable()); |
| ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); |
| |
| // Fill tensors |
| fill(AccessorType(src), 0); |
| fill(AccessorType(_weights_target), 1); |
| fill(AccessorType(_bias_target), 2); |
| |
| // Populate and validate memory manager |
| _ms.clear(); |
| _ms.populate(1); |
| _ms.mg.acquire(); |
| |
| // Compute NEConvolutionLayer function |
| _f_target->run(); |
| _ms.mg.release(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> run_reference(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info) |
| { |
| // Create reference |
| SimpleTensor<T> src{ input_shape, _data_type, 1 }; |
| SimpleTensor<T> weights{ weights_shape, _data_type, 1 }; |
| SimpleTensor<T> bias{ bias_shape, _data_type, 1 }; |
| |
| // Fill reference |
| fill(src, 0); |
| fill(weights, 1); |
| fill(bias, 2); |
| |
| return reference::convolution_layer<T>(src, weights, bias, output_shape, info); |
| } |
| |
| public: |
| unsigned int num_iterations{ 0 }; |
| SimpleTensor<T> dst_ref{}; |
| TensorType dst_target{}; |
| |
| private: |
| DataType _data_type{ DataType::UNKNOWN }; |
| DataLayout _data_layout{ DataLayout::UNKNOWN }; |
| PadStrideInfo _info{}; |
| std::vector<TensorShape> _input_shapes{}; |
| std::vector<TensorShape> _output_shapes{}; |
| TensorShape _weights_shape{}; |
| TensorShape _bias_shape{}; |
| MemoryManagementServiceType _ms{}; |
| TensorType _weights_target{}; |
| TensorType _bias_target{}; |
| std::unique_ptr<ComplexFunctionType> _f_target{}; |
| }; |
| |
| /** Fixture that create a pipeline of Convolutions and changes the inputs dynamically |
| * |
| * Runs a list of convolutions and then resizes the inputs and reruns. |
| * Updates the memory manager and allocated memory. |
| */ |
| template <typename TensorType, |
| typename AccessorType, |
| typename MemoryManagementServiceType, |
| typename ComplexFunctionType> |
| class DynamicTensorType2PipelineFunction : public framework::Fixture |
| { |
| using T = float; |
| |
| public: |
| template <typename...> |
| void setup(std::vector<TensorShape> input_shapes) |
| { |
| _data_type = DataType::F32; |
| _data_layout = DataLayout::NHWC; |
| _input_shapes = input_shapes; |
| |
| run(); |
| } |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| switch(tensor.data_type()) |
| { |
| case DataType::F32: |
| { |
| std::uniform_real_distribution<float> distribution(-1.0f, 1.0f); |
| library->fill(tensor, distribution, i); |
| break; |
| } |
| default: |
| library->fill_tensor_uniform(tensor, i); |
| } |
| } |
| |
| void run() |
| { |
| const unsigned int num_functions = 5; |
| const unsigned int num_tensors = num_functions + 1; |
| const unsigned int num_resizes = _input_shapes.size(); |
| |
| for(unsigned int i = 0; i < num_functions; ++i) |
| { |
| _functions.emplace_back(std::make_unique<ComplexFunctionType>(_ms.mm)); |
| } |
| |
| for(unsigned int i = 0; i < num_resizes; ++i) |
| { |
| TensorShape input_shape = _input_shapes[i]; |
| TensorShape weights_shape = TensorShape(3U, 3U, input_shape[2], input_shape[2]); |
| TensorShape output_shape = input_shape; |
| PadStrideInfo info(1U, 1U, 1U, 1U); |
| |
| if(_data_layout == DataLayout::NHWC) |
| { |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| permute(output_shape, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| std::vector<TensorType> tensors(num_tensors); |
| std::vector<TensorType> ws(num_functions); |
| std::vector<TensorType> bs(num_functions); |
| |
| auto tensor_info = TensorInfo(input_shape, 1, _data_type); |
| auto weights_info = TensorInfo(weights_shape, 1, _data_type); |
| tensor_info.set_data_layout(_data_layout); |
| weights_info.set_data_layout(_data_layout); |
| |
| tensors[0].allocator()->init(tensor_info); |
| for(unsigned int f = 0; f < num_functions; ++f) |
| { |
| tensors[f + 1].allocator()->init(tensor_info); |
| ws[f].allocator()->init(weights_info); |
| |
| _functions[f]->configure(&tensors[f], &ws[f], nullptr, &tensors[f + 1], info); |
| |
| // Allocate tensors |
| tensors[f].allocator()->allocate(); |
| ws[f].allocator()->allocate(); |
| } |
| tensors[num_functions].allocator()->allocate(); |
| |
| // Populate and validate memory manager |
| _ms.clear(); |
| _ms.populate(1); |
| _ms.mg.acquire(); |
| |
| // Run pipeline |
| for(unsigned int f = 0; f < num_functions; ++f) |
| { |
| _functions[f]->run(); |
| } |
| |
| // Release memory group |
| _ms.mg.release(); |
| } |
| } |
| |
| private: |
| DataType _data_type{ DataType::UNKNOWN }; |
| DataLayout _data_layout{ DataLayout::UNKNOWN }; |
| std::vector<TensorShape> _input_shapes{}; |
| MemoryManagementServiceType _ms{}; |
| std::vector<std::unique_ptr<ComplexFunctionType>> _functions{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_UNIT_DYNAMIC_TENSOR */ |