aboutsummaryrefslogtreecommitdiff
path: root/src/backends/cl/workloads/ClBatchNormalizationFloatWorkload.cpp
blob: 992abc2f5697c6db7696098d42df747d49b3c679 (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
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
//
// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include "ClBatchNormalizationFloatWorkload.hpp"
#include "ClWorkloadUtils.hpp"

#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <aclCommon/ArmComputeUtils.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <cl/ClLayerSupport.hpp>
#include <cl/ClTensorHandle.hpp>

namespace armnn
{
using namespace armcomputetensorutils;

arm_compute::Status ClBatchNormalizationValidate(const TensorInfo& input,
                                                 const TensorInfo& output,
                                                 const TensorInfo& mean,
                                                 const TensorInfo& var,
                                                 const TensorInfo& beta,
                                                 const TensorInfo& gamma,
                                                 const BatchNormalizationDescriptor& descriptor,
                                                 const ActivationDescriptor* activationDescriptor)
{
    const arm_compute::TensorInfo aclInputInfo =
        armcomputetensorutils::BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
    const arm_compute::TensorInfo aclOutputInfo =
        armcomputetensorutils::BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
    const arm_compute::TensorInfo aclMeanInfo =
        armcomputetensorutils::BuildArmComputeTensorInfo(mean, descriptor.m_DataLayout);
    const arm_compute::TensorInfo aclVarInfo =
        armcomputetensorutils::BuildArmComputeTensorInfo(var, descriptor.m_DataLayout);
    const arm_compute::TensorInfo aclBetaInfo =
        armcomputetensorutils::BuildArmComputeTensorInfo(beta, descriptor.m_DataLayout);
    const arm_compute::TensorInfo aclGammaInfo =
        armcomputetensorutils::BuildArmComputeTensorInfo(gamma, descriptor.m_DataLayout);

    const arm_compute::ActivationLayerInfo activationInfo = ConvertActivationDescriptorToAclActivationLayerInfo(
            activationDescriptor);

    return arm_compute::CLBatchNormalizationLayer::validate(&aclInputInfo,
                                                            &aclOutputInfo,
                                                            &aclMeanInfo,
                                                            &aclVarInfo,
                                                            &aclBetaInfo,
                                                            &aclGammaInfo,
                                                            descriptor.m_Eps,
                                                            activationInfo);
}

ClBatchNormalizationFloatWorkload::ClBatchNormalizationFloatWorkload(
    const BatchNormalizationQueueDescriptor& descriptor,
    const WorkloadInfo& info,
    const arm_compute::CLCompileContext& clCompileContext)
    : FloatWorkload<BatchNormalizationQueueDescriptor>(descriptor, info)
{
    // Report Profiling Details
    ARMNN_REPORT_PROFILING_WORKLOAD_DESC("ClBatchNormalizationWorkload_Construct",
                                         descriptor.m_Parameters,
                                         info,
                                         this->GetGuid());

    m_Mean = std::make_unique<arm_compute::CLTensor>();
    BuildArmComputeTensor(*m_Mean, m_Data.m_Mean->GetTensorInfo());

    m_Variance = std::make_unique<arm_compute::CLTensor>();
    BuildArmComputeTensor(*m_Variance, m_Data.m_Variance->GetTensorInfo());

    m_Gamma = std::make_unique<arm_compute::CLTensor>();
    BuildArmComputeTensor(*m_Gamma, m_Data.m_Gamma->GetTensorInfo());

    m_Beta = std::make_unique<arm_compute::CLTensor>();
    BuildArmComputeTensor(*m_Beta, m_Data.m_Beta->GetTensorInfo());

    m_Data.ValidateInputsOutputs("ClBatchNormalizationFloatWorkload", 1, 1);

    arm_compute::ICLTensor& input  = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
    arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();

    arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
    input.info()->set_data_layout(aclDataLayout);
    output.info()->set_data_layout(aclDataLayout);

    const arm_compute::ActivationLayerInfo activationInfo = ConvertAdditionalInfoToAclActivationLayerInfo(descriptor);

    {
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClBatchNormalizationFloatWorkload_configure");
        m_Layer.configure(clCompileContext,
                          &input,
                          &output,
                          m_Mean.get(),
                          m_Variance.get(),
                          m_Beta.get(),
                          m_Gamma.get(),
                          m_Data.m_Parameters.m_Eps,
                          activationInfo);
    }

    InitializeArmComputeClTensorData(*m_Mean, m_Data.m_Mean);
    InitializeArmComputeClTensorData(*m_Variance, m_Data.m_Variance);
    InitializeArmComputeClTensorData(*m_Beta, m_Data.m_Beta);
    InitializeArmComputeClTensorData(*m_Gamma, m_Data.m_Gamma);

    // Force Compute Library to perform the necessary copying and reshaping, after which
    // delete all the input tensors that will no longer be needed
    m_Layer.prepare();
    FreeUnusedTensors();
}

void ClBatchNormalizationFloatWorkload::Execute() const
{
    ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClBatchNormalizationFloatWorkload_Execute", this->GetGuid());
    RunClFunction(m_Layer, CHECK_LOCATION());
}

void ClBatchNormalizationFloatWorkload::FreeUnusedTensors()
{
    FreeTensorIfUnused(m_Mean);
    FreeTensorIfUnused(m_Variance);
    FreeTensorIfUnused(m_Gamma);
    FreeTensorIfUnused(m_Beta);
}

} //namespace armnn