blob: 20386b5d86b39d1ca1062e818899adfa803ff5ea (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
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
|
