| /* |
| * Copyright (c) 2017-2021 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_MEMORY_MANAGER |
| #define ARM_COMPUTE_TEST_UNIT_MEMORY_MANAGER |
| |
| #include "arm_compute/core/TensorShape.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/BlobLifetimeManager.h" |
| #include "arm_compute/runtime/MemoryManagerOnDemand.h" |
| #include "arm_compute/runtime/PoolManager.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/FullyConnectedLayer.h" |
| #include "tests/validation/reference/SoftmaxLayer.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| /** Simple test case to run two fully connected layers using a blob affinity memory manager |
| * |
| * Runs two fully connected layers back to back |
| */ |
| template <typename TensorType, typename AccessorType, typename AllocatorType, typename FullyConnectedFunction> |
| class BlobMemoryManagerSimpleTestCaseFixture : public framework::Fixture |
| { |
| using T = float; |
| |
| public: |
| void setup() |
| { |
| _target = compute_target(); |
| _reference = compute_reference(); |
| }; |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| std::uniform_real_distribution<> distribution(0.5f, 1.f); |
| library->fill(tensor, distribution, i); |
| } |
| |
| TensorType compute_target() |
| { |
| auto lifetime_mgr = std::make_shared<BlobLifetimeManager>(); |
| auto pool_mgr = std::make_shared<PoolManager>(); |
| auto mm = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr, pool_mgr); |
| |
| // Create tensors |
| TensorType w1 = create_tensor<TensorType>(TensorShape(128U, 128U), DataType::F32, 1); |
| TensorType b1 = create_tensor<TensorType>(TensorShape(128U), DataType::F32, 1); |
| TensorType w2 = create_tensor<TensorType>(TensorShape(128U, 24U), DataType::F32, 1); |
| TensorType b2 = create_tensor<TensorType>(TensorShape(24U), DataType::F32, 1); |
| TensorType src = create_tensor<TensorType>(TensorShape(128U), DataType::F32, 1); |
| TensorType fc1 = create_tensor<TensorType>(TensorShape(128U), DataType::F32, 1); |
| TensorType dst = create_tensor<TensorType>(TensorShape(24U), DataType::F32, 1); |
| |
| // Create and configure function |
| FullyConnectedFunction fc_layer_1(mm); |
| FullyConnectedFunction fc_layer_2(mm); |
| fc_layer_1.configure(&src, &w1, &b1, &fc1); |
| fc_layer_2.configure(&fc1, &w2, &b2, &dst); |
| |
| // Allocate tensors |
| w1.allocator()->allocate(); |
| b1.allocator()->allocate(); |
| w2.allocator()->allocate(); |
| b2.allocator()->allocate(); |
| src.allocator()->allocate(); |
| fc1.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| // Finalize memory manager |
| mm->populate(_allocator, 1 /* num_pools */); |
| ARM_COMPUTE_ASSERT(mm->lifetime_manager()->are_all_finalized()); |
| ARM_COMPUTE_ASSERT(mm->pool_manager()->num_pools() == 1); |
| |
| // Fill tensors |
| fill(AccessorType(src), 0); |
| fill(AccessorType(w1), 1); |
| fill(AccessorType(b1), 2); |
| fill(AccessorType(w2), 3); |
| fill(AccessorType(b2), 4); |
| |
| // Compute functions |
| fc_layer_1.run(); |
| fc_layer_2.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference() |
| { |
| // Create reference |
| SimpleTensor<T> w1{ TensorShape(128U, 128U), DataType::F32 }; |
| SimpleTensor<T> b1{ TensorShape(128U), DataType::F32 }; |
| SimpleTensor<T> w2{ TensorShape(128U, 24U), DataType::F32 }; |
| SimpleTensor<T> b2{ TensorShape(24U), DataType::F32 }; |
| SimpleTensor<T> src{ TensorShape(128U), DataType::F32 }; |
| |
| // Fill reference |
| fill(src, 0); |
| fill(w1, 1); |
| fill(b1, 2); |
| fill(w2, 3); |
| fill(b2, 4); |
| |
| auto fc1 = reference::fully_connected_layer(src, w1, b1, TensorShape(128U)); |
| return reference::fully_connected_layer(fc1, w2, b2, TensorShape(24U)); |
| } |
| |
| protected: |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| AllocatorType _allocator{}; |
| }; |
| |
| /** Test case to run two fully connected layers using a blob affinity memory manager, |
| * reconfigure with different shapes and rerun |
| * |
| * Runs two fully connected layers back to back then reconfigures with different batch size and reruns |
| * Shapes of the reconfigure step are smaller that the initial configured step |
| */ |
| template <typename TensorType, typename AccessorType, typename AllocatorType, typename FullyConnectedFunction> |
| class BlobMemoryManagerReconfigureTestCaseFixture : public framework::Fixture |
| { |
| using T = float; |
| |
| public: |
| void setup() |
| { |
| _max_batches = 8; |
| _cur_batches = 6; |
| _target = compute_target(); |
| _reference = compute_reference(); |
| }; |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| std::uniform_real_distribution<> distribution(0.5f, 1.f); |
| library->fill(tensor, distribution, i); |
| } |
| |
| TensorType compute_target() |
| { |
| AllocatorType allocator{}; |
| auto lifetime_mgr = std::make_shared<BlobLifetimeManager>(); |
| auto pool_mgr = std::make_shared<PoolManager>(); |
| auto mm = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr, pool_mgr); |
| |
| // Create tensors |
| TensorType w1 = create_tensor<TensorType>(TensorShape(128U, 128U), DataType::F32, 1); |
| TensorType b1 = create_tensor<TensorType>(TensorShape(128U), DataType::F32, 1); |
| TensorType w2 = create_tensor<TensorType>(TensorShape(128U, 24U), DataType::F32, 1); |
| TensorType b2 = create_tensor<TensorType>(TensorShape(24U), DataType::F32, 1); |
| TensorType src = create_tensor<TensorType>(TensorShape(128U, _max_batches), DataType::F32, 1); |
| TensorType fc1 = create_tensor<TensorType>(TensorShape(128U, _max_batches), DataType::F32, 1); |
| TensorType dst = create_tensor<TensorType>(TensorShape(24U, _max_batches), DataType::F32, 1); |
| |
| // Create and configure function |
| FullyConnectedFunction fc_layer_1(mm); |
| FullyConnectedFunction fc_layer_2(mm); |
| fc_layer_1.configure(&src, &w1, &b1, &fc1); |
| fc_layer_2.configure(&fc1, &w2, &b2, &dst); |
| |
| // Allocate persistent tensors |
| w1.allocator()->allocate(); |
| b1.allocator()->allocate(); |
| w2.allocator()->allocate(); |
| b2.allocator()->allocate(); |
| |
| // Allocate tensors (1st iteration) |
| src.allocator()->allocate(); |
| fc1.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| // Finalize memory manager |
| mm->populate(_allocator, 1 /* num_pools */); |
| ARM_COMPUTE_ASSERT(mm->lifetime_manager()->are_all_finalized()); |
| ARM_COMPUTE_ASSERT(mm->pool_manager()->num_pools() == 1); |
| |
| // Fill tensors (1st iteration) |
| fill(AccessorType(src), 0); |
| fill(AccessorType(w1), 1); |
| fill(AccessorType(b1), 2); |
| fill(AccessorType(w2), 3); |
| fill(AccessorType(b2), 4); |
| |
| // Compute functions (1st iteration) |
| fc_layer_1.run(); |
| fc_layer_2.run(); |
| |
| // Update tensor shapes (2nd iteration) |
| auto src_padding = src.allocator()->info().padding(); |
| auto fc1_padding = fc1.allocator()->info().padding(); |
| auto dst_padding = dst.allocator()->info().padding(); |
| int diff = _max_batches - _cur_batches; |
| auto new_src_padding = PaddingSize(src_padding.top, src_padding.right, src_padding.bottom + diff, src_padding.left); |
| auto new_fc1_padding = PaddingSize(fc1_padding.top, fc1_padding.right, fc1_padding.bottom + diff, fc1_padding.left); |
| auto new_dst_padding = PaddingSize(dst_padding.top, dst_padding.right, dst_padding.bottom + diff, dst_padding.left); |
| src.allocator()->info().set_tensor_shape(TensorShape(128U, _cur_batches)).set_is_resizable(true).extend_padding(new_src_padding); |
| src.allocator()->info().set_is_resizable(false); |
| fc1.allocator()->info().set_tensor_shape(TensorShape(128U, _cur_batches)).set_is_resizable(true).extend_padding(new_fc1_padding); |
| fc1.allocator()->info().set_is_resizable(false); |
| dst.allocator()->info().set_tensor_shape(TensorShape(24U, _cur_batches)).set_is_resizable(true).extend_padding(new_dst_padding); |
| dst.allocator()->info().set_is_resizable(false); |
| |
| // Configure FC info |
| FullyConnectedLayerInfo fc_info; |
| fc_info.retain_internal_weights = true; |
| |
| // Configure functions (2nd iteration) |
| fc_layer_1.configure(&src, &w1, &b1, &fc1, fc_info); |
| fc_layer_2.configure(&fc1, &w2, &b2, &dst, fc_info); |
| |
| // Fill tensors (2nd iteration) |
| fill(AccessorType(src), 5); |
| |
| // Compute functions (2nd iteration) |
| fc_layer_1.run(); |
| fc_layer_2.run(); |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference() |
| { |
| // Create reference |
| SimpleTensor<T> w1{ TensorShape(128U, 128U), DataType::F32 }; |
| SimpleTensor<T> b1{ TensorShape(128U), DataType::F32 }; |
| SimpleTensor<T> w2{ TensorShape(128U, 24U), DataType::F32 }; |
| SimpleTensor<T> b2{ TensorShape(24U), DataType::F32 }; |
| SimpleTensor<T> src{ TensorShape(128U, _cur_batches), DataType::F32 }; |
| |
| // Fill reference |
| fill(src, 5); |
| fill(w1, 1); |
| fill(b1, 2); |
| fill(w2, 3); |
| fill(b2, 4); |
| |
| auto fc1 = reference::fully_connected_layer(src, w1, b1, TensorShape(128U, _cur_batches)); |
| return reference::fully_connected_layer(fc1, w2, b2, TensorShape(24U, _cur_batches)); |
| } |
| |
| protected: |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| AllocatorType _allocator{}; |
| unsigned int _max_batches{}; |
| unsigned int _cur_batches{}; |
| }; |
| |
| /** Test case to run a fully connected layer followed by a softmax layer using a blob affinity memory manager, |
| * reconfigure with different shapes and rerun |
| * |
| * Runs a fully connected convolution layer followed by a softmax layer then reconfigures with different batch size and reruns |
| * Shapes of the reconfigure step are smaller that the initial configured step |
| */ |
| template <typename TensorType, typename AccessorType, typename AllocatorType, typename FullyConnectedFunction, typename SoftmaxFunction> |
| class BlobMemoryManagerReconfigure2TestCaseFixture : public framework::Fixture |
| { |
| using T = float; |
| |
| public: |
| void setup() |
| { |
| _max_batches = 30; |
| _cur_batches = 3; |
| _target = compute_target(); |
| _reference = compute_reference(); |
| }; |
| |
| protected: |
| template <typename U> |
| void fill(U &&tensor, int i) |
| { |
| std::uniform_real_distribution<> distribution(0.5f, 1.f); |
| library->fill(tensor, distribution, i); |
| } |
| |
| TensorType compute_target() |
| { |
| AllocatorType allocator{}; |
| auto lifetime_mgr = std::make_shared<BlobLifetimeManager>(); |
| auto pool_mgr = std::make_shared<PoolManager>(); |
| auto mm = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr, pool_mgr); |
| |
| // Create tensors |
| TensorType w = create_tensor<TensorType>(TensorShape(112U, 8U), DataType::F32, 1); |
| TensorType b = create_tensor<TensorType>(TensorShape(8U), DataType::F32, 1); |
| TensorType src = create_tensor<TensorType>(TensorShape(1U, 1U, 112U, _max_batches), DataType::F32, 1); |
| TensorType fc = create_tensor<TensorType>(TensorShape(8U, _max_batches), DataType::F32, 1); |
| TensorType dst = create_tensor<TensorType>(TensorShape(8U, _max_batches), DataType::F32, 1); |
| |
| // Create and configure function |
| FullyConnectedFunction fc_layer(mm); |
| SoftmaxFunction smx_layer(mm); |
| fc_layer.configure(&src, &w, &b, &fc); |
| smx_layer.configure(&fc, &dst); |
| |
| // Allocate persistent tensors |
| w.allocator()->allocate(); |
| b.allocator()->allocate(); |
| |
| // Allocate tensors (1st iteration) |
| src.allocator()->allocate(); |
| fc.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| // Finalize memory manager |
| mm->populate(_allocator, 1 /* num_pools */); |
| ARM_COMPUTE_ASSERT(mm->lifetime_manager()->are_all_finalized()); |
| ARM_COMPUTE_ASSERT(mm->pool_manager()->num_pools() == 1); |
| |
| // Fill tensors (1st iteration) |
| fill(AccessorType(src), 0); |
| fill(AccessorType(w), 1); |
| fill(AccessorType(b), 2); |
| |
| // Compute functions (1st iteration) |
| fc_layer.run(); |
| smx_layer.run(); |
| |
| // Get padding requirements |
| auto fc_padding = fc.allocator()->info().padding(); |
| |
| // Configure FC info |
| FullyConnectedLayerInfo fc_info; |
| fc_info.retain_internal_weights = true; |
| |
| // Run rest iterations |
| for(int i = _max_batches; i >= static_cast<int>(_cur_batches); --i) |
| { |
| int diff = _max_batches - i; |
| auto new_fc_padding = PaddingSize(fc_padding.top, fc_padding.right, fc_padding.bottom + diff, fc_padding.left); |
| src.allocator()->info().set_tensor_shape(TensorShape(1U, 1U, 112U, i)); |
| fc.allocator()->info().set_tensor_shape(TensorShape(8U, i)).set_is_resizable(true).extend_padding(new_fc_padding); |
| fc.allocator()->info().set_is_resizable(false); |
| dst.allocator()->info().set_tensor_shape(TensorShape(8U, i)); |
| |
| // Configure functions |
| fc_layer.configure(&src, &w, &b, &fc, fc_info); |
| smx_layer.configure(&fc, &dst); |
| |
| // Fill tensors |
| fill(AccessorType(src), 3); |
| |
| // Compute functions |
| fc_layer.run(); |
| smx_layer.run(); |
| } |
| |
| return dst; |
| } |
| |
| SimpleTensor<T> compute_reference() |
| { |
| // Create reference |
| SimpleTensor<T> w{ TensorShape(112U, 8U), DataType::F32 }; |
| SimpleTensor<T> b{ TensorShape(8U), DataType::F32 }; |
| SimpleTensor<T> src{ TensorShape(1U, 1U, 112U, _cur_batches), DataType::F32 }; |
| |
| // Fill reference |
| fill(src, 3); |
| fill(w, 1); |
| fill(b, 2); |
| |
| auto fc = reference::fully_connected_layer(src, w, b, TensorShape(8U, _cur_batches)); |
| return reference::softmax_layer(fc, 1.f); |
| } |
| |
| protected: |
| TensorType _target{}; |
| SimpleTensor<T> _reference{}; |
| AllocatorType _allocator{}; |
| unsigned int _max_batches{}; |
| unsigned int _cur_batches{}; |
| }; |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_TEST_UNIT_MEMORY_MANAGER */ |