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
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
|
//
// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <LayersFwd.hpp>
#include <Network.hpp>
#include <NetworkUtils.hpp>
#include <Optimizer.hpp>
#include <TestUtils.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <doctest/doctest.h>
TEST_SUITE("Optimizer")
{
using namespace armnn;
using namespace armnn::optimizations;
TEST_CASE("FuseConvertFp32Fp16intoConst")
{
Graph graph;
const unsigned int shape[] = {1, 2, 2, 3};
const TensorInfo constTensorInfo(4, shape, DataType::Float32, 1.0, 0, true);
const TensorInfo outputConvertInfo(4, shape, DataType::BFloat16, 1.0, 0, true);
ConstantLayer* constantLayer = graph.AddLayer<ConstantLayer>("constant");
std::vector<float> constantValues(constTensorInfo.GetNumElements(), 3.1416f);
ConstTensor constTensor(constTensorInfo, constantValues.data());
constantLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(constTensor);
constantLayer->GetOutputSlot().SetTensorInfo(constTensorInfo);
ConvertFp32ToBf16Layer* convertLayer = graph.AddLayer<ConvertFp32ToBf16Layer>("convert");
convertLayer->GetOutputSlot().SetTensorInfo(outputConvertInfo);
OutputLayer* output = graph.AddLayer<OutputLayer>(0, "output");
// Connect up constant -> convert -> output
constantLayer->GetOutputSlot().Connect(convertLayer->GetInputSlot(0));
convertLayer->GetOutputSlot().Connect(output->GetInputSlot(0));
auto checkConstantFloat32 = [](const armnn::Layer *const layer) -> bool {
return IsLayerOfType<ConstantLayer>(layer) &&
(layer->GetDataType() == DataType::Float32);
};
auto checkConstantBFloat16 = [](const armnn::Layer *const layer) -> bool {
return IsLayerOfType<ConstantLayer>(layer) &&
(layer->GetDataType() == DataType::BFloat16);
};
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
checkConstantFloat32,
&IsLayerOfType<ConvertFp32ToBf16Layer>,
&IsLayerOfType<OutputLayer>));
armnn::Optimizer::Pass(graph, MakeOptimizations(FuseConversionLayersIntoConstLayers()));
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
checkConstantBFloat16,
&IsLayerOfType<OutputLayer>));
}
TEST_CASE("RevertConstantWeightsToFP32")
{
Graph graph;
const unsigned int shape[] = {1, 2, 2, 3};
const TensorInfo constTensorInfo(4, shape, DataType::Float32, 1.0, 0, true);
const TensorInfo outputConvertInfo(4, shape, DataType::BFloat16, 1.0, 0, true);
TensorInfo inputInfo(4, shape, DataType::Float32);
auto* input = graph.AddLayer<InputLayer>(0, "input0");
input->GetOutputSlot().SetTensorInfo(inputInfo);
auto* constantLayer = graph.AddLayer<ConstantLayer>("constant");
std::vector<float> constantValues(constTensorInfo.GetNumElements(), 3.1416f);
ConstTensor constTensor(constTensorInfo, constantValues.data());
constantLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(constTensor);
constantLayer->GetOutputSlot().SetTensorInfo(constTensorInfo);
ConvertFp32ToBf16Layer* convertLayerInputs = graph.AddLayer<ConvertFp32ToBf16Layer>("convert");
convertLayerInputs->GetOutputSlot().SetTensorInfo(outputConvertInfo);
ConvertFp32ToBf16Layer* convertLayerWeights = graph.AddLayer<ConvertFp32ToBf16Layer>("convert2");
convertLayerWeights->GetOutputSlot().SetTensorInfo(outputConvertInfo);
ConvertFp32ToBf16Layer* convertLayerBiases = graph.AddLayer<ConvertFp32ToBf16Layer>("convert3");
convertLayerBiases->GetOutputSlot().SetTensorInfo(outputConvertInfo);
auto* biases = graph.AddLayer<armnn::ConstantLayer>("Biases");
biases->m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(constTensor);
biases->GetOutputSlot().SetTensorInfo(constTensorInfo);
armnn::Convolution2dDescriptor descriptor;
descriptor.m_BiasEnabled = true;
auto* conv = graph.AddLayer<armnn::Convolution2dLayer>(descriptor, "conv2d");
const armnn::TensorInfo infoFP32({ 2, 3, 8, 1 }, armnn::DataType::Float32);
conv->GetOutputSlot().SetTensorInfo(infoFP32);
auto* output = graph.AddLayer<OutputLayer>(0, "output");
// Connect up Input -> Convert ->
// Constant -> Convert -> Conv2d -> Output
// Constant -> Convert ->
input->GetOutputSlot().Connect(convertLayerInputs->GetInputSlot(0));
constantLayer->GetOutputSlot().Connect(convertLayerWeights->GetInputSlot(0));
biases->GetOutputSlot().Connect(convertLayerBiases->GetInputSlot(0));
convertLayerInputs->GetOutputSlot().Connect(conv->GetInputSlot(0));
convertLayerWeights->GetOutputSlot().Connect(conv->GetInputSlot(1));
convertLayerBiases->GetOutputSlot().Connect(conv->GetInputSlot(2));
conv->GetOutputSlot().Connect(output->GetInputSlot(0));
auto checkConstantFloat32 = [](const armnn::Layer *const layer) -> bool {
return IsLayerOfType<ConstantLayer>(layer) &&
(layer->GetDataType() == DataType::Float32);
};
auto checkConstantBFloat16 = [](const armnn::Layer *const layer) -> bool {
return IsLayerOfType<ConstantLayer>(layer) &&
(layer->GetDataType() == DataType::BFloat16);
};
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
checkConstantFloat32,
checkConstantFloat32,
&IsLayerOfType<ConvertFp32ToBf16Layer>,
&IsLayerOfType<ConvertFp32ToBf16Layer>,
&IsLayerOfType<ConvertFp32ToBf16Layer>,
&IsLayerOfType<Convolution2dLayer>,
&IsLayerOfType<OutputLayer>));
armnn::Optimizer::Pass(graph, MakeOptimizations(FuseConversionLayersIntoConstLayers()));
bool revert = RevertConstantWeightsToFP32(conv);
// Erase unconnected layer as occurs during Topological Sort.
graph.EraseLayer(convertLayerInputs);
CHECK(revert);
CHECK(constantLayer->GetDataType() == DataType::Float32);
CHECK(CheckSequence(graph.cbegin(), graph.cend(),
&IsLayerOfType<InputLayer>,
checkConstantBFloat16,
checkConstantFloat32,
&IsLayerOfType<Convolution2dLayer>,
&IsLayerOfType<OutputLayer>));
}
}
|
