10 #include <TestUtils.hpp> 14 #include <doctest/doctest.h> 18 using namespace armnn;
21 TEST_CASE(
"FuseConvertFp32Fp16intoConst")
24 const unsigned int shape[] = {1, 2, 2, 3};
30 std::vector<float> constantValues(constTensorInfo.
GetNumElements(), 3.1416f);
31 ConstTensor constTensor(constTensorInfo, constantValues.data());
32 constantLayer->
m_LayerOutput = std::make_shared<ScopedTensorHandle>(constTensor);
44 auto checkConstantFloat32 = [](
const armnn::Layer *
const layer) ->
bool {
45 return IsLayerOfType<ConstantLayer>(layer) &&
48 auto checkConstantBFloat16 = [](
const armnn::Layer *
const layer) ->
bool {
49 return IsLayerOfType<ConstantLayer>(layer) &&
55 &IsLayerOfType<ConvertFp32ToBf16Layer>,
56 &IsLayerOfType<OutputLayer>));
61 checkConstantBFloat16,
62 &IsLayerOfType<OutputLayer>));
65 TEST_CASE(
"RevertConstantWeightsToFP32")
68 const unsigned int shape[] = {1, 2, 2, 3};
78 std::vector<float> constantValues(constTensorInfo.
GetNumElements(), 3.1416f);
79 ConstTensor constTensor(constTensorInfo, constantValues.data());
80 constantLayer->m_LayerOutput = std::make_shared<ScopedTensorHandle>(constTensor);
81 constantLayer->GetOutputSlot().SetTensorInfo(constTensorInfo);
91 biases->
m_LayerOutput = std::make_unique<armnn::ScopedTensorHandle>(constTensor);
92 biases->GetOutputSlot().SetTensorInfo(constTensorInfo);
98 conv->GetOutputSlot().SetTensorInfo(infoFP32);
106 constantLayer->GetOutputSlot().Connect(convertLayerWeights->
GetInputSlot(0));
107 biases->GetOutputSlot().Connect(convertLayerBiases->
GetInputSlot(0));
113 conv->GetOutputSlot().Connect(output->GetInputSlot(0));
115 auto checkConstantFloat32 = [](
const armnn::Layer *
const layer) ->
bool {
116 return IsLayerOfType<ConstantLayer>(layer) &&
119 auto checkConstantBFloat16 = [](
const armnn::Layer *
const layer) ->
bool {
120 return IsLayerOfType<ConstantLayer>(layer) &&
125 &IsLayerOfType<InputLayer>,
126 checkConstantFloat32,
127 checkConstantFloat32,
128 &IsLayerOfType<ConvertFp32ToBf16Layer>,
129 &IsLayerOfType<ConvertFp32ToBf16Layer>,
130 &IsLayerOfType<ConvertFp32ToBf16Layer>,
131 &IsLayerOfType<Convolution2dLayer>,
132 &IsLayerOfType<OutputLayer>));
145 &IsLayerOfType<InputLayer>,
146 checkConstantBFloat16,
147 checkConstantFloat32,
148 &IsLayerOfType<Convolution2dLayer>,
149 &IsLayerOfType<OutputLayer>));
A layer that the constant data can be bound to.
bool m_BiasEnabled
Enable/disable bias.
Optimizer::Optimizations MakeOptimizations(Args &&... args)
bool CheckSequence(const armnn::Graph::ConstIterator first, const armnn::Graph::ConstIterator last)
std::shared_ptr< ConstTensorHandle > m_LayerOutput
LayerT * AddLayer(Args &&... args)
Adds a new layer, of type LayerType, to the graph constructed with the arguments passed.
ConstIterator cbegin() const
Returns const iterator pointing to the beginning of the list. Lowercase for range-based for loops...
A Convolution2dDescriptor for the Convolution2dLayer.
int Connect(InputSlot &destination)
void EraseLayer(Iterator pos)
Deletes the layer at the specified position.
static void Pass(Graph &graph, const Optimizations &optimizations)
Copyright (c) 2021 ARM Limited and Contributors.
const InputSlot & GetInputSlot(unsigned int index) const override
Get a const input slot handle by slot index.
A layer user-provided data can be bound to (e.g. inputs, outputs).
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
bool RevertConstantWeightsToFP32(Layer *layer)
void SetTensorInfo(const TensorInfo &tensorInfo) override
const OutputSlot & GetOutputSlot(unsigned int index=0) const override
Get the const output slot handle by slot index.
ConstIterator cend() const
Returns const iterator pointing to the end of the list. Lowercase for range-based for loops...
This layer represents a convolution 2d operation.
This layer converts data type Float32 to BFloat16.
OptimizeForConnection< ConstantLayer, ConvertFp32ToBf16Layer, FuseConvertFp32ToBf16IntoConstLayers > FuseConversionLayersIntoConstLayers
unsigned int GetNumElements() const