| // |
| // Copyright © 2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "GpuFsaPooling2d.hpp" |
| #include "UtilsGpuFsa.hpp" |
| |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| |
| #include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h> |
| #include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h> |
| #include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuPool2d.h> |
| #include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h> |
| |
| using namespace arm_compute::experimental::dynamic_fusion; |
| using namespace armnn::armcomputetensorutils; |
| |
| namespace armnn |
| { |
| |
| arm_compute::Status GpuFsaPooling2dValidate(const TensorInfo& input, |
| const Pooling2dDescriptor& descriptor) |
| { |
| // Create a new workload sketch, for validation purposes |
| auto compileCtx = arm_compute::CLKernelLibrary::get().get_compile_context(); |
| auto workloadContext = GpuWorkloadContext(&compileCtx); |
| GpuWorkloadSketch sketch{ &workloadContext }; |
| |
| arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); |
| aclInputInfo.set_are_values_constant(input.IsConstant()); |
| arm_compute::ITensorInfo* inputInfo = workloadContext.create_tensor_info(aclInputInfo); |
| |
| Pool2dAttributes pool2dAttributes = CreatePool2dAttributes(descriptor); |
| GpuPool2dSettings pool2dSettings{}; |
| |
| return GpuPool2d::validate_op(sketch, inputInfo, pool2dAttributes, pool2dSettings); |
| } |
| |
| void GpuFsaPooling2dCreateOp(GpuFsaPreCompiledBlob* blob, |
| const TensorInfo& input, |
| const Pooling2dDescriptor& descriptor) |
| { |
| GpuWorkloadSketch* sketch = blob->sketch.get(); |
| GpuWorkloadContext* workloadContext = blob->workloadContext.get(); |
| std::vector<arm_compute::ITensorInfo*> inputTensorInfos = {}; |
| std::vector<arm_compute::ITensorInfo*> outputTensorInfos = {}; |
| |
| arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); |
| aclInputInfo.set_are_values_constant(input.IsConstant()); |
| |
| inputTensorInfos.emplace_back(workloadContext->create_tensor_info(aclInputInfo)); |
| |
| Pool2dAttributes pool2dAttributes = CreatePool2dAttributes(descriptor); |
| GpuPool2dSettings pool2dSettings{}; |
| |
| // Validate operator, check status and update reasonIfUnsupported |
| arm_compute::Status aclStatus = GpuPool2d::validate_op(*sketch, |
| inputTensorInfos[0], |
| pool2dAttributes, |
| pool2dSettings); |
| |
| const bool supported = aclStatus.error_code() == arm_compute::ErrorCode::OK; |
| if (!supported) |
| { |
| throw BackendCapabilityException("\"GpuFsa\" backend failed during pooling 2d validation"); |
| } |
| |
| arm_compute::ITensorInfo* addOutputInfo = GpuPool2d::create_op(*sketch, |
| inputTensorInfos[0], |
| pool2dAttributes, |
| pool2dSettings); |
| |
| // Temporary fix until fusing attempt is make for GpuFsa backend and Output layer workload is created. |
| outputTensorInfos.emplace_back(workloadContext->create_tensor_info()); |
| GpuOutput::create_op(*sketch, addOutputInfo, outputTensorInfos[0]); |
| |
| // Store the TensorInfos within the blob as unique_ptrs to be used later |
| blob->inputTensorInfos = std::make_unique<std::vector<arm_compute::ITensorInfo*>>(inputTensorInfos); |
| blob->outputTensorInfos = std::make_unique<std::vector<arm_compute::ITensorInfo*>>(outputTensorInfos); |
| } |
| |
| } // namespace armnn |