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Diffstat (limited to 'src/backends/gpuFsa/workloads/GpuFsaPreCompiledWorkload.cpp')
| -rw-r--r-- | src/backends/gpuFsa/workloads/GpuFsaPreCompiledWorkload.cpp | 106 |
1 files changed, 106 insertions, 0 deletions
diff --git a/src/backends/gpuFsa/workloads/GpuFsaPreCompiledWorkload.cpp b/src/backends/gpuFsa/workloads/GpuFsaPreCompiledWorkload.cpp new file mode 100644 index 0000000000..20386b5d86 --- /dev/null +++ b/src/backends/gpuFsa/workloads/GpuFsaPreCompiledWorkload.cpp @@ -0,0 +1,106 @@ +// +// Copyright © 2024 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "GpuFsaPreCompiledWorkload.hpp" +#include "GpuFsaWorkloadUtils.hpp" +#include "armnn/utility/PolymorphicDowncast.hpp" + +#include <gpuFsa/GpuFsaTensorHandle.hpp> +#include <gpuFsa/GpuFsaBackend.hpp> +#include <aclCommon/ArmComputeTensorUtils.hpp> + +#include <aclCommon/ArmComputeTensorUtils.hpp> +#include <arm_compute/runtime/CL/CLTensor.h> +#include <arm_compute/core/ITensorInfo.h> +#include <arm_compute/core/TensorInfo.h> +#include <arm_compute/core/TensorShape.h> +#include <arm_compute/core/CL/CLKernelLibrary.h> +#include <arm_compute/core/CL/CLCompileContext.h> + +#include <arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h> +#include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadContext.h> +#include <src/dynamic_fusion/sketch/gpu/GpuWorkloadContextImpl.h> +#include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h> +#include <arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h> +#include <arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h> + +namespace armnn { + +GpuFsaPreCompiledWorkload::GpuFsaPreCompiledWorkload(const PreCompiledQueueDescriptor &descriptor, + const WorkloadInfo &info) + : BaseWorkload<PreCompiledQueueDescriptor>(descriptor, info), m_workloadInfo(info) +{ + // Check that the workload is holding a pointer to a valid pre-compiled object + if (m_Data.m_PreCompiledObject == nullptr) + { + throw InvalidArgumentException( + "GpuFsaPrecompiledWorkload requires a valid pre-compiled object (GpuWorkloadSketch)."); + } +} + +void GpuFsaPreCompiledWorkload::Execute() const +{ +/* + * The Execute function of the GpuFsa Backends PreCompiled workload needs to jump through various hoops in order to + * create a valid sketch and runtime that can execute the kernel + * First we need all of the data stored within the PreCompiled blob which was used to setup the workload, namely: + * The GpuWorkloadContext, this is a context which contains the TensorInfos and is unique to the graph being run + * The Sketch, this can contain one or many ops and acts as a subgraph within the context + * The TensorInfoIds, These are the ids of the TensorInfos used during the creation of the Sketch and stored within + * the context. + * It is very important that the Tensors passed into the Runtime being used to execute this sketch are created with + * the same TensorInfos as used when creating the sketch. We do this by creating new tensors, getting the original + * TensorInfos from the GpuWorkloadContext via their ids, and then importing the buffers from our own TensorHandles + * directly into these newly created Tensors. This allows us to link the externally visible Tensors from ArmNN to the + * Tensors which are needed to execute with the Sketch. + * + */ + using namespace arm_compute::experimental::dynamic_fusion; + // Get the runtime and configure it with the precompiled sketch + ClWorkloadRuntime runtime; + GpuFsaPreCompiledBlob *preCompiledBlob = static_cast<GpuFsaPreCompiledBlob*>(m_Data.m_PreCompiledObject); + auto workloadContext = + &(preCompiledBlob->workloadContext->implementation()); + auto sketch = preCompiledBlob->sketch.release(); + std::vector<int32_t> inputIds = *(preCompiledBlob->inputIds.get()); + std::vector<int32_t> outputIds = *(preCompiledBlob->outputIds.get()); + auto status = runtime.configure(*sketch); + + // (Important) Allocate auxiliary tensor memory if there are any + for(auto &data : runtime.get_auxiliary_tensors()) + { + arm_compute::CLTensor* tensor = std::get<0>(data); + arm_compute::TensorInfo info = std::get<1>(data); + arm_compute::experimental::dynamic_fusion::AuxMemoryInfo aux_mem_req = std::get<2>(data); + tensor->allocator()->init(info, aux_mem_req.alignment); + tensor->allocator()->allocate(); // Use ACL allocated memory + } + + // Create and initialize user tensors + std::vector<arm_compute::CLTensor*> inputsWeightsOutputs; + inputsWeightsOutputs.reserve(m_Data.m_Inputs.size() + m_Data.m_Outputs.size()); + + for (uint32_t inputSlotIdx = 0; inputSlotIdx < m_Data.m_Inputs.size(); ++inputSlotIdx) + { + arm_compute::CLTensor* input = new arm_compute::CLTensor{}; + input->allocator()->init(*(dynamic_cast<arm_compute::TensorInfo*>( + workloadContext->get_tensor_info(inputIds[inputSlotIdx])))); + auto* inputHandle = PolymorphicDowncast<GpuFsaTensorHandle*>(m_Data.m_Inputs[inputSlotIdx]); + input->allocator()->import_memory(inputHandle->GetTensor().cl_buffer()); + inputsWeightsOutputs.emplace_back(std::move(input)); + } + // Set the outputs + for (uint32_t outputSlotIdx = 0; outputSlotIdx < m_Data.m_Outputs.size(); ++outputSlotIdx) + { + arm_compute::CLTensor* output = new arm_compute::CLTensor{}; + output->allocator()->init(*(dynamic_cast<arm_compute::TensorInfo*>( + workloadContext->get_tensor_info(outputIds[outputSlotIdx])))); + auto* outputHandle = PolymorphicDowncast<GpuFsaTensorHandle*>(m_Data.m_Outputs[outputSlotIdx]); + output->allocator()->import_memory(outputHandle->GetTensor().cl_buffer()); + inputsWeightsOutputs.emplace_back(std::move(output)); + } + runtime.run(inputsWeightsOutputs); +} +} // namespace armnn
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