loco Namespace Reference

Data Structures
class	AnnotatedItem
class	AvgPool2D
	2D Average Pooling More...
class	BiasAdd
	Produce a value of domain D from an input value (of domain D) and a bias. More...
class	BiasAdd< Domain::Feature >
	Add Feature and Bias along "depth" axis. More...
class	BiasAdd< Domain::Tensor >
	Add Tensor and Bias. More...
class	BiasDecode
	Create a "Tensor" from a "Bias". More...
class	BiasEncode
	Create a "Bias" from a "Tensor". More...
class	BiasShape
	Bias Shape. More...
class	CanonicalDialect
	A singleton for Canonical Dialect. More...
struct	CanonicalNode
struct	CanonicalNodeDef
struct	CanonicalNodeMutableVisitor
struct	CanonicalNodeMutableVisitorBase
struct	CanonicalNodeVisitor
struct	CanonicalNodeVisitorBase
struct	CanonicalShapeInferenceRule
	Shape inference rule for canonical dialect. More...
struct	CanonicalTypeInferenceRule
	Type Inference Rule for Canonical Dialect. More...
class	ConstGen
	Create a value from constant byte array. More...
class	Conv2D
	2D Spatial Convolution More...
struct	DataTypeImpl
	C++ scalar type corresponding to each DataType. More...
struct	DataTypeImpl< DataType::BOOL >
struct	DataTypeImpl< DataType::FLOAT16 >
struct	DataTypeImpl< DataType::FLOAT32 >
struct	DataTypeImpl< DataType::FLOAT64 >
struct	DataTypeImpl< DataType::S16 >
struct	DataTypeImpl< DataType::S32 >
struct	DataTypeImpl< DataType::S4 >
struct	DataTypeImpl< DataType::S64 >
struct	DataTypeImpl< DataType::S8 >
struct	DataTypeImpl< DataType::STRING >
struct	DataTypeImpl< DataType::U16 >
struct	DataTypeImpl< DataType::U32 >
struct	DataTypeImpl< DataType::U4 >
struct	DataTypeImpl< DataType::U64 >
struct	DataTypeImpl< DataType::U8 >
class	DepthwiseConv2D
	Depthwise 2D Convolution. More...
class	DepthwiseFilterDecode
	Create a tensor from a depthwise filter. More...
struct	DepthwiseFilterDecoder
	Describe how to build a tensor from a depthwise convolution filter. More...
class	DepthwiseFilterEncode
	Create a depthwise filter from a tensor. More...
struct	DepthwiseFilterEncoder
	Describe how to build a depthwise convolution filter from a tensor. More...
class	DepthwiseFilterIndex
	DepthwiseFilter Index. More...
class	DepthwiseFilterShape
	DepthwiseFilter Shape. More...
class	Dialect
	Dialect interface. More...
struct	DialectService
	Dialect Service interface. More...
class	Dimension
	The value of one dimension in a tensor shape. More...
class	EltwiseAdd
	Elementwise Add lhs and rhs. More...
class	EltwiseDiv
	Elementwise Div lhs and rhs. More...
class	EltwiseMax
	Elementwise Maximum of lhs and rhs. More...
class	EltwiseMul
	Elementwise Mul lhs and rhs. More...
class	EltwiseSqrt
	Elementwise Sqrt of input. More...
class	EltwiseSub
	Elementwise Sub lhs and rhs. More...
class	ErrorDetail
	The details of each error. More...
class	ErrorDetail< ErrorCategory::MissingArgument >
	The details of MissingArgument error. More...
struct	ErrorListener
	Error listener (with default implementation) More...
class	FeatureDecode
	Create a tensor from a feature map. More...
struct	FeatureDecoder
	Describe how to build a tensor from a (convolution) feature map. More...
class	FeatureEncode
	Create a feature map from a tensor. More...
struct	FeatureEncoder
	Decribe how to build a (convolution) feature map from a tensor. More...
class	FeatureIndex
	Feature Index. More...
class	FeatureShape
	Feature Map Shape. More...
class	FilterDecode
	Create a tensor from a filter. More...
struct	FilterDecoder
	Decribe how to build a a tensor from a filter. More...
class	FilterEncode
	Create a filter from a tensor. More...
struct	FilterEncoder
	Decribe how to build a (convolution) filter from a tensor. More...
class	FilterIndex
	Filter Index. More...
class	FilterShape
	Filter Shape. More...
struct	FixedArity
class	Forward
	Create a new value identical to its input. More...
class	Graph
	A neural network graph. More...
class	GraphInput
	Graph-level Input Metadata. More...
struct	GraphInputIndexQueryService
class	GraphOutput
	Graph-level Output Metadata. More...
struct	GraphOutputIndexQueryService
struct	IErrorListener
	Error listener interface. More...
class	MatMul
	Matrix Multiplication lhs and rhs. More...
class	MatrixDecode
	Create Tensor from Matrix. More...
struct	MatrixDecoder
	Describe how to build a tensor from a matrix. More...
class	MatrixEncode
	Create Matrix from Tensor. More...
struct	MatrixEncoder
	Decribe how to build a matrix from a tensor. More...
class	MatrixIndex
	Matrix Index. More...
class	MatrixShape
	Matrix Shape. More...
class	MaxPool2D
	2D Max Pooling More...
class	Mixin
class	Mixin< Trait::DataTyped >
class	Mixin< Trait::TensorShaped >
class	MultiDialectShapeInferenceRule
	Shape inference rule for multiple dialects. More...
class	MultiDialectTypeInferenceRule
	Type Inference Rule for multiple dialects. More...
class	NamedEntity
	Trait for elements with name. More...
class	Node
	Logical unit of computation. More...
struct	NodeAnnotation
	Extensible Node Metadata. More...
class	NodeMixin
class	NodeMixin< NodeTrait::DataType >
class	NodeMixin< NodeTrait::TensorShape >
class	NodePool
class	NodeShape
class	ObjectPool
	Object Pool. More...
class	Padding2D
class	PaddingND
class	Permutation
class	Permutation< Domain::DepthwiseFilter >
	Mapping between DepthwiseFilter/Tensor Axis. More...
class	Permutation< Domain::Feature >
	Mapping between Feature/Tensor Axis. More...
class	Permutation< Domain::Filter >
	Mapping between Filter/Tensor Axis. More...
class	Permutation< Domain::Matrix >
	Mapping between Matrix/Tensor Axis. More...
class	PermutingDecoder
class	PermutingDecoder< Domain::DepthwiseFilter >
	Permutation-based DepthwiseFilter-to-Tensor converter. More...
class	PermutingDecoder< Domain::Feature >
class	PermutingDecoder< Domain::Filter >
	Permutation-based Filter-to-Tensor converter. More...
class	PermutingDecoder< Domain::Matrix >
	Permutation-based Matrix-to-Tensor converter. More...
class	PermutingEncoder
class	PermutingEncoder< Domain::DepthwiseFilter >
	Permutation-based Tensor-to-DepthwiseFilter converter. More...
class	PermutingEncoder< Domain::Feature >
class	PermutingEncoder< Domain::Filter >
	Permutation-based Tensor-to-Filter converter. More...
class	PermutingEncoder< Domain::Matrix >
	Permutation-based Tensor-to-Matrix converter. More...
class	Pull
	Create a value from user data. More...
class	Push
	Make a value visible to user. More...
class	ReLU
	Create a new value that rectifies its input. More...
class	ReLU6
	Create a new value that rectifies its input capping the units at 6. More...
class	Reshape
class	Reshape< ReshapeType::Fixed >
	Reshape a tensor to another tensor whose shape is known at compile time. More...
struct	ShapeInference
struct	ShapeInferenceRule
class	ShapeInferenceSession
class	Softmax
	Computes softmax activations. More...
class	Softmax< Domain::Tensor >
	Computes softmax activations for Tensor domain. More...
class	Stride
	Stride configuration for N-dimensional spatial operations. More...
class	Stride< 2 >
	Stride configuration for 2D spatial operations. More...
class	Subst
class	Subst< SubstQualifier::Default >
	A helper for below "replace" helper. More...
class	Tanh
	Create a new value that rectifies its input by tanh. More...
class	TensorAxisSet
class	TensorBroadcast
	Duplicate elements along specified axes. More...
class	TensorConcat
	Concatenate two tensors. More...
class	TensorConstantPad
	Pads a tensor with constant value. More...
class	TensorReduce
	Computes ReduceFunc operations for Tensor domain. More...
class	TensorShape
class	TensorTranspose
	Permute an input. More...
class	TransposedConv2D
	2D Transposed Convolution More...
struct	TypeInference
struct	TypeInferenceRule
class	TypeInferenceSession
class	Use
	The edge between a node definition and its user. More...
class	Window
	ND Receptive Field Shape. More...
class	Window< 2 >
	2D Receptive Field Shape More...
struct	With

Typedefs
using	GraphInputIndex = uint32_t
using	GraphOutputIndex = uint32_t
using	FixedReshape = Reshape< ReshapeType::Fixed >
using	TensorSoftmax = Softmax< Domain::Tensor >
using	TensorBiasAdd = BiasAdd< Domain::Tensor >
using	FeatureBiasAdd = BiasAdd< Domain::Feature >
using	TensorAxis = uint32_t
using	TensorIndex = nncc::core::ADT::tensor::Index

Enumerations
enum class	CanonicalOpcode { CANONICAL_NODE }
	Canonical Node Opcode. More...
enum class	DataType {   Unknown , U4 , U8 , U16 ,   U32 , U64 , S4 , S8 ,   S16 , S32 , S64 , FLOAT16 ,   FLOAT32 , FLOAT64 , BOOL , STRING ,   MXFP4 , MXINT8 }
	"scalar" value type More...
enum class	DepthwiseFilterAxis { Depth , Multiplier , Height , Width }
enum class	Domain {   Unknown , Tensor , Feature , Filter ,   DepthwiseFilter , Bias , Matrix }
	Describe the kind of (N-dimensional) loco values. More...
enum class	FeatureAxis { Count , Depth , Height , Width }
enum class	FilterAxis { Count , Depth , Height , Width }
enum class	Trait { DataTyped , TensorShaped }
enum class	MatrixAxis { Height , Width }
enum class	SubstQualifier { Default }
enum class	NodeTrait { DataType , TensorShape }
enum class	ReshapeType { Fixed }
enum class	ReduceFunc { Mean }
	Reduce type functions. More...
enum class	ErrorCategory { MissingArgument }
	Possible error categories. More...

Functions
std::ostream &	operator<< (std::ostream &os, const loco::FeatureShape &feature_shape)
	dump FeatureShape values to stream
std::ostream &	operator<< (std::ostream &os, const loco::FilterShape &filter_shape)
	dump FilterShape values to stream
std::ostream &	operator<< (std::ostream &os, const loco::TensorShape &tensor_shape)
	dump TensorShape values to stream
std::ostream &	operator<< (std::ostream &os, const loco::Padding2D &pad)
	dump Padding2D values to stream
std::vector< loco::Node * >	postorder_traversal (const std::vector< loco::Node * > &roots)
	Generate postorder traversal sequence starting from "roots".
std::set< loco::Node * >	active_nodes (const std::vector< loco::Node * > &roots)
	Enumerate all the nodes required to compute "roots".
template<typename T , typename ARG >
T	_must_cast (ARG arg)
uint32_t	size (DataType data_type)
	Returns the size of the data type.
bool	operator== (const Dimension &, const Dimension &)
	Equality operator between two Dimensions.
bool	operator== (const Dimension &, uint32_t)
bool	operator== (uint32_t, const Dimension &)
Dimension	make_dimension (void)
	Make an "unknown" dimension.
template<typename T >
T	must_cast (FeatureEncoder *node)
	A helper dynamic_cast that throws when failed.
template<typename T >
T	must_cast (const FeatureEncoder *node)
template<typename T >
T	must_cast (FeatureDecoder *node)
template<typename T >
T	must_cast (const FeatureDecoder *node)
template<typename T >
T	must_cast (FilterEncoder *node)
	A helper dynamic_cast that throws when failed.
template<typename T >
T	must_cast (const FilterEncoder *node)
std::vector< Node * >	input_nodes (const Graph *)
std::vector< Node * >	output_nodes (Graph *)
std::set< Node * >	all_nodes (Graph *)
	Enumerate all the nodes in a given graph.
std::unique_ptr< Graph >	make_graph (void)
std::set< Node * >	preds (const Node *node)
	Enumerate all the predecessors of a given node.
std::set< Node * >	succs (const Node *node)
	Enumerate all the successors of a given node.
Subst< SubstQualifier::Default >	replace (Node *node)
template<typename T >
T	must_cast (Node *node)
	A helper dynamic_cast that throws when failed.
template<typename T >
T	must_cast (const Node *node)
void	link (GraphOutput *, Push *push)
Push *	push_node (Graph *g, const GraphOutputIndex &index)
	Find a Push node with a given output index.
void	link (GraphInput *, Pull *pull)
Pull *	pull_node (Graph *g, const GraphInputIndex &index)
	Find a Pull node with a given input index.
bool	operator== (const NodeShape &lhs, const NodeShape &rhs)
uint32_t	element_count (const loco::TensorShape *tensor_shape)
	Return the number of elements in a tensor of given shape.
bool	operator== (const TensorShape &lhs, const TensorShape &rhs)
	'==' operator for TensorShape
bool	valid (Graph *g, std::unique_ptr< ErrorListener > &&l=nullptr)
	Validate a loco graph.
ShapeInferenceSession	apply (ShapeInferenceRule *r)
bool	shape_known (const Node *node)
NodeShape	shape_get (const Node *node)
void	shape_erase (Node *node)
TypeInferenceSession	apply (TypeInferenceRule *r)
bool	dtype_known (const Node *node)
DataType	dtype_get (const Node *node)
void	dtype_erase (Node *node)
	INSTANTIATE (DataType::S32)
	INSTANTIATE (DataType::FLOAT32)
std::ostream &	operator<< (std::ostream &os, const loco::Dimension &d)

Detailed Description

Push

Pull

ConstGen

TensorBroadcast

Typedef Documentation

FeatureBiasAdd

using loco::FeatureBiasAdd = typedef BiasAdd<Domain::Feature>

Definition at line 829 of file Nodes.h.

FixedReshape

using loco::FixedReshape = typedef Reshape<ReshapeType::Fixed>

Definition at line 523 of file Nodes.h.

GraphInputIndex

using loco::GraphInputIndex = typedef uint32_t

Definition at line 25 of file GraphInputIndex.h.

GraphOutputIndex

using loco::GraphOutputIndex = typedef uint32_t

Definition at line 25 of file GraphOutputIndex.h.

TensorAxis

using loco::TensorAxis = typedef uint32_t

Definition at line 25 of file TensorAxis.h.

TensorBiasAdd

using loco::TensorBiasAdd = typedef BiasAdd<Domain::Tensor>

Definition at line 806 of file Nodes.h.

TensorIndex

using loco::TensorIndex = typedef nncc::core::ADT::tensor::Index

Definition at line 26 of file TensorIndex.h.

TensorSoftmax

using loco::TensorSoftmax = typedef Softmax<Domain::Tensor>

Definition at line 737 of file Nodes.h.

Enumeration Type Documentation

CanonicalOpcode

enum class loco::CanonicalOpcode

strong

Canonical Node Opcode.

WARNING The order is subject to change. DO NOT serialize this value.

Enumerator
CANONICAL_NODE

Definition at line 28 of file CanonicalOpcode.h.

{
#define CANONICAL_NODE(OPCODE, CLASS) OPCODE,
#include "CanonicalNodes.lst"
#undef CANONICAL_NODE
};

DataType

enum class loco::DataType

strong

"scalar" value type

Enumerator
Unknown
U4
U8
U16
U32
U64
S4
S8
S16
S32
S64
FLOAT16
FLOAT32
FLOAT64
BOOL
STRING
MXFP4
MXINT8

Definition at line 26 of file DataType.h.

{
  Unknown, // Unknown type (serves as a default value)
 
  U4,  // 4-bit unsigned integer
  U8,  // 8-bit unsigned integer
  U16, // 16-bit unsigned integer
  U32, // 32-bit unsigned integer
  U64, // 64-bit unsigned integer
 
  S4,  // 4-bit signed integer
  S8,  // 8-bit signed integer
  S16, // 16-bit signed integer
  S32, // 32-bit signed integer
  S64, // 64-bit signed integer
 
  FLOAT16, // IEEE 16-bit floating-point
  FLOAT32, // IEEE 32-bit floating-point
  FLOAT64, // IEEE 64-bit floating-point
 
  // WARNING the size of Bool may vary for NN frameworks
  // TODO we need to find a way to resolve this issue
  BOOL, // Boolean
 
  // WARNING STRING is NOT fully supported yet
  STRING, // String
 
  // Microscaling data type
  MXFP4,
  MXINT8,
};

DepthwiseFilterAxis

enum class loco::DepthwiseFilterAxis

strong

Enumerator
Depth
Multiplier
Height
Width

Definition at line 23 of file DepthwiseFilterAxis.h.

{
  Depth,
  Multiplier,
  Height,
  Width
};

Domain

enum class loco::Domain

strong

Describe the kind of (N-dimensional) loco values.

loco is an intermediate representation for neural network compiler, which mainly focuses on N-dimensional values (usually referred to as Tensor).

There are several special cases for N-dimensional values according to its usage. For example, vision community often refers to 4D array as "FeatureMap".

It is definitely possible to represent all of these special cases using Tensor, but that scheme may introduces some confusion (e.g. NCHW vs NHWC issue).

loco distinguishes these special cases from Tensor in order to reduce such confusion.

This "Domain" enum class enumerates all of these special cases that loco supports.

Enumerator
Unknown
Tensor
Feature
Filter
DepthwiseFilter
Bias
Matrix

Definition at line 39 of file Domain.h.

{
  Unknown,
  Tensor,
  Feature,
  Filter,          /* 2D Convolution Filter */
  DepthwiseFilter, /* Depthwise 2D Convolution Filter */
  Bias,
  Matrix,
  /* ... */
};

ErrorCategory

enum class loco::ErrorCategory

strong

Possible error categories.

This enum class enumerates all the possible validation failure reasons.

WARN DO NOT serialize this code. The tag value is subject to change.

Enumerator
MissingArgument

Definition at line 34 of file Verifier.h.

{
  MissingArgument,
  /* TO BE ADDED */
};

FeatureAxis

enum class loco::FeatureAxis

strong

Enumerator
Count
Depth
Height
Width

Definition at line 23 of file FeatureAxis.h.

{
  Count,
  Depth,
  Height,
  Width
};

FilterAxis

enum class loco::FilterAxis

strong

Enumerator
Count
Depth
Height
Width

Definition at line 23 of file FilterAxis.h.

{
  Count,
  Depth,
  Height,
  Width
};

MatrixAxis

enum class loco::MatrixAxis

strong

Enumerator
Height
Width

Definition at line 23 of file MatrixAxis.h.

{
  Height,
  Width
};

NodeTrait

enum class loco::NodeTrait

strong

Enumerator
DataType
TensorShape

Definition at line 30 of file NodeMixins.h.

{
  DataType,
  // Nodes with TensorShape trait will provide the following methods:
  // - rank()
  // - rank(value)
  // - dim()
  // - dim(value)
  // - shape({...})
  TensorShape,
};

ReduceFunc

enum class loco::ReduceFunc

strong

Reduce type functions.

Enumerator
Mean

Definition at line 608 of file Nodes.h.

{
  Mean, // ReduceMean
  // TODO Support other reduce operations
};

ReshapeType

enum class loco::ReshapeType

strong

Enumerator
Fixed

Definition at line 489 of file Nodes.h.

{
  Fixed, // shape is known at compile time
  // Add another type for a case when shape is not known at compile time
};

SubstQualifier

enum class loco::SubstQualifier

strong

Enumerator
Default

Definition at line 43 of file Node.h.

{
  Default, // Replace all the occurrences as "Use" (by default)
};

Trait

enum class loco::Trait

strong

Enumerator
DataTyped
TensorShaped

Definition at line 39 of file Graph.h.

{
  // Any "DataTyped" class has the following methods
  // - DataType dtype(void) const;
  // - void dtype(const DataType &value);
  DataTyped,
  // Any "TensorShaped" class has the following methods
  // - const TensorShape *shape(void) const;
  // - void shape(std::unique_ptr<TensorShape> &&);
  // - void shape(std::initializer_list<Dimension> &&);
  //
  // TODO Rename NodeMixin::TensorShape as NodeMixin::NDShape
  TensorShaped,
};

Function Documentation

_must_cast()

template<typename T , typename ARG >

T loco::_must_cast

(

ARG

arg

)

Definition at line 28 of file CastHelpers.h.

{
  auto cast_arg = dynamic_cast<T>(arg);
  if (cast_arg == nullptr)
  {
    std::string msg = "loco::must_cast() failed to cast: ";
    msg += typeid(T).name();
    throw std::invalid_argument(msg.c_str());
  }
  return cast_arg;
}

active_nodes()

std::set< loco::Node * > loco::active_nodes

(

const std::vector< loco::Node * > &

roots

)

Enumerate all the nodes required to compute "roots".

Definition at line 112 of file Algorithm.cpp.

{
  // This implementation works but may be inefficient
  //
  // TODO Use efficient implementation if necessary
  auto nodes = postorder_traversal(roots);
  return std::set<loco::Node *>{nodes.begin(), nodes.end()};
}

References postorder_traversal().

Referenced by fme_apply::check_patterns_valid(), fme_detect::EqualizePatternFinder::find(), moco::test::find_nodes_bytype(), luci::DeadNodeQueryServiceImpl::isDeadNode(), luci::layer_info_map(), mpqsolver::pattern::Q8LayerNormWithQ16VarianceResolver::resolve(), mpqsolver::pattern::Q8SoftmaxWithQ16SubExpResolver::resolve(), mpqsolver::bisection::BisectionSolver::run(), logo::RemoveDeadNodePass::run(), logo::RemoveDeadNodeWithQueryPass::run(), exo::FoldReshapeOfConstPass::run(), exo::FoldTransposeOfConstPass::run(), exo::FuseBiasAddPass::run(), exo::FuseInstanceNormPass::run(), exo::FuseReluPass::run(), exo::FuseRsqrtPass::run(), exo::FuseSquaredDifferencePass::run(), logo::SimplifyDomainConversionPass::run(), luci::CanonicalizePass::run(), luci::ConvertNCHWToNHWCPass::run(), luci::ConvertToFakeQuantizedModelPass::run(), luci::DecomposeHardSwishPass::run(), luci::DecomposeSoftmaxPass::run(), luci::ExpandBroadcastConstPass::run(), luci::FoldAddV2Pass::run(), luci::FoldCastPass::run(), luci::FoldDensifyPass::run(), luci::FoldDepthwiseConv2DPass::run(), luci::FoldDequantizePass::run(), luci::FoldFullyConnectedPass::run(), luci::FoldGatherPass::run(), luci::FoldMulPass::run(), luci::FoldReshapePass::run(), luci::FoldShapePass::run(), luci::FoldSparseToDensePass::run(), luci::FoldSqueezePass::run(), luci::ForwardReshapeToUnaryOpPass::run(), luci::ForwardTransposeOpPass::run(), luci::FuseActivationFunctionPass::run(), luci::FuseAddToFullyConnectedBiasPass::run(), luci::FuseAddWithConvPass::run(), luci::FuseAddWithFullyConnectedPass::run(), luci::FuseAddWithTConvPass::run(), luci::FuseBatchNormWithConvPass::run(), luci::FuseBatchNormWithDwConvPass::run(), luci::FuseBatchNormWithTConvPass::run(), luci::FuseGeluPass::run(), luci::FuseHorizontalFullyConnectedPass::run(), luci::FuseInstanceNormPass::run(), luci::FuseMeanWithMeanPass::run(), luci::FuseMulToFullyConnectedWeightsPass::run(), luci::FuseMulWithConvPass::run(), luci::FuseMulWithDivPass::run(), luci::FuseMulWithFullyConnectedPass::run(), luci::FusePreActivationBatchNormPass::run(), luci::FusePReluPass::run(), luci::FuseRmsNormPass::run(), luci::FuseRoPEPass::run(), luci::FuseRsqrtPass::run(), luci::FuseSliceWithTConvPass::run(), luci::FuseTransposeWithMeanPass::run(), luci::MakeBatchNormGammaPositivePass::run(), luci::PropagateQParamForwardPass::run(), luci::RemoveDuplicateConstPass::run(), luci::RemoveFakeQuantPass::run(), luci::RemoveGatherGuardPass::run(), luci::RemoveQuantDequantSeqPass::run(), luci::RemoveRedundantDequantizePass::run(), luci::RemoveRedundantReshapePass::run(), luci::RemoveRedundantTransposePass::run(), luci::RemoveUnnecessaryAddPass::run(), luci::RemoveUnnecessaryCastPass::run(), luci::RemoveUnnecessaryReshapeNetPass::run(), luci::RemoveUnnecessaryReshapePass::run(), luci::RemoveUnnecessarySlicePass::run(), luci::RemoveUnnecessarySplitPass::run(), luci::RemoveUnnecessaryStridedSlicePass::run(), luci::RemoveUnnecessaryTransposeNetPass::run(), luci::ReplaceMulAddWithDepthwiseConvPass::run(), luci::ReplaceNonConstFCWithBatchMatMulPass::run(), luci::ReplaceSubWithAddPass::run(), luci::ReplaceWithFCGeluFCPass::run(), luci::ResolveCustomOpAddPass::run(), luci::ResolveCustomOpBatchMatMulPass::run(), luci::ResolveCustomOpMatMulPass::run(), luci::ResolveCustomOpMaxPoolWithArgmaxPass::run(), luci::ResolveCustomOpSplitVPass::run(), luci::ResolveFormerCustomOpPass::run(), luci::ShuffleWeightTo16x1Float32Pass::run(), luci::SparsifyTensorPass::run(), luci::SubstituteExpandDimsToReshapePass::run(), luci::SubstitutePackToReshapePass::run(), luci::SubstitutePadV2ToPadPass::run(), luci::SubstituteSplitVToSplitPass::run(), luci::SubstituteSqueezeToReshapePass::run(), luci::SubstituteStridedSliceToReshapePass::run(), luci::SubstituteTransposeToReshapePass::run(), luci::TransformMinMaxToRelu6Pass::run(), luci::TransformMinReluToRelu6Pass::run(), luci::TransformSqrtDivToRsqrtMulPass::run(), luci::UnrollUnidirectionalSequenceLSTMPass::run(), luci::XpSepActFromTransposeConvPass::run(), luci::QuantizeOnnxDequantizeLinearPass::run(), luci::QuantizeOnnxQDQPass::run(), luci::QuantizeWithPredecessorPass::run(), exo::CanonicalNodeConverter< CanonicalType >::run(), exo::TensorBroadcastConverter::run(), exo::MergeConcatNodesPass::run(), fme_apply::FusePostScalePass::run(), fme_apply::FusePreScalePass::run(), luci::CommonSubExpressionEliminationPass::run(), luci::CopyQuantParamPass::run(), luci::ForceQuantParamPass::run(), luci::QuantizeDequantizeWeightsPass::run(), luci::QuantizeDequantizeWeightsWithGPTQPass::run(), luci::QuantizeOnnxFakeQuantModelPass::run(), luci::QuantizeWeightsPass::run(), luci::QuantizeWithMinMaxPass::run(), luci::RequantizePass::run(), luci::QuantizePreCheckerPass::run(), moco::tf::SimpleNodeTransform< ConcreteNode >::run(), moco::ConstantFoldAdd::run(), moco::ConstantFoldMul::run(), moco::ConstantFoldPack::run(), moco::ConstantFoldStridedSlice::run(), moco::FuseBinaryIntoPreceding::run(), moco::ResolveConstantShape::run(), moco::ResolveFusedBatchNorm::run(), moco::ResolveReshapeWildcardDim::run(), moco::ResolveSquaredDifference::run(), moco::SqueezeReduceNode::run(), luci::QuantizedModelVerifier::verify(), and q_implant::QImplant::write().

all_nodes()

std::set< loco::Node * > loco::all_nodes

(

loco::Graph *

g

)

Enumerate all the nodes in a given graph.

NOTE This method returns std::set<Node *> unlike input_nodes and output_nodes.

Please use traverse algorithms that "Algorithm.h" provides (such as postorder_traversal) if order is relevant for implementation.

Definition at line 59 of file Graph.cpp.

{
  std::set<loco::Node *> res;
 
  for (uint32_t n = 0; n < g->nodes()->size(); ++n)
  {
    res.insert(g->nodes()->at(n));
  }
 
  return res;
}

References size.

Referenced by locop::FormattedGraphImpl< Formatter::LinearV1 >::dump(), luci::inference_candidates(), luci_interpreter::GraphLoader::loadOperators(), logo::RemoveDeadNodePass::run(), logo::RemoveDeadNodeWithQueryPass::run(), logo::RemoveForwardNodePass::run(), moco::RemoveTFIdentityNode::run(), luci::QuantizeWithMinMaxPass::run(), and q_implant::QImplant::write().

apply() [1/2]

ShapeInferenceSession loco::apply ( ShapeInferenceRule *  r )

inline

Definition at line 51 of file ShapeInference.h.

{ return ShapeInferenceSession{r}; }

Referenced by exo::convert_to_TFLNodes(), exo::ShapeInferencePass::run(), exo::TypeInferencePass::run(), moco::tf::ShapeInferencePass::run(), and moco::tf::TypeInferencePass::run().

apply() [2/2]

TypeInferenceSession loco::apply ( TypeInferenceRule *  r )

inline

Definition at line 99 of file TypeInference.h.

{ return TypeInferenceSession{r}; }

dtype_erase()

void loco::dtype_erase ( Node *  node )

inline

Definition at line 110 of file TypeInference.h.

{ TypeInference::erase(node); }

References loco::TypeInference::erase().

dtype_get()

DataType loco::dtype_get ( const Node *  node )

inline

Definition at line 109 of file TypeInference.h.

{ return TypeInference::get(node); }

References loco::TypeInference::get().

Referenced by exo::Conv2DConverter::convert(), exo::DepthwiseConv2DConverter::convert(), exo::MatMulConverter::convert(), locop::FormattedGraphImpl< Formatter::LinearV1 >::dump(), and exo::circle_detail::TypeInference::get().

dtype_known()

bool loco::dtype_known ( const Node *  node )

inline

Definition at line 108 of file TypeInference.h.

{ return TypeInference::known(node); }

References loco::TypeInference::known().

Referenced by exo::Conv2DConverter::convert(), exo::DepthwiseConv2DConverter::convert(), exo::MatMulConverter::convert(), locop::FormattedGraphImpl< Formatter::LinearV1 >::dump(), exo::circle_detail::TypeInference::get(), and loco::TypeInferenceSession::to().

element_count()

uint32_t loco::element_count

(

const loco::TensorShape *

tensor_shape

)

Return the number of elements in a tensor of given shape.

NOTE 1.

"volume" returns 1 if the rank is 0.

NOTE 2.

"caller" SHOULD pass a valid shape that has no unknown dimension.

• The behavior of "volume" on invalid is undefined.

Definition at line 24 of file TensorShape.cpp.

{
  uint32_t res = 1;
 
  for (uint32_t axis = 0; axis < tensor_shape->rank(); ++axis)
  {
    // Let's use "assert" here as "caller" is responsible for this check.
    // Please refer to the header for details.
    assert(tensor_shape->dim(axis).known());
    res *= tensor_shape->dim(axis).value();
  }
 
  return res;
}

References loco::TensorShape::dim(), loco::Dimension::known(), loco::TensorShape::rank(), and loco::Dimension::value().

input_nodes()

std::vector< Node * > loco::input_nodes

(

const Graph *

g

)

Definition at line 71 of file Graph.cpp.

{
  std::map<GraphInputIndex, loco::Node *> table;
 
  for (uint32_t n = 0; n < g->nodes()->size(); ++n)
  {
    auto node = g->nodes()->at(n);
 
    if (auto service = node->dialect()->service<GraphInputIndexQueryService>())
    {
      if (service->associated(node))
      {
        auto input_index = service->index(node);
        assert(table.find(input_index) == table.end());
        table[input_index] = node;
      }
    }
  }
 
  std::vector<loco::Node *> res;
 
  for (uint32_t n = 0; n < g->inputs()->size(); ++n)
  {
    auto it = table.find(n);
    res.emplace_back(it == table.end() ? nullptr : it->second);
  }
 
  return res;
}

References size.

Referenced by mpqsolver::bisection::compute_depth(), record_minmax::DirectoryIterator::DirectoryIterator(), minmax_embedder::Embedder::embed(), onert::odc::Embedder::embed(), entry(), mpqsolver::core::H5FileDataProvider::H5FileDataProvider(), record_minmax::HDF5Iterator::HDF5Iterator(), luci_interpreter::GraphLoader::initInputOutputTensors(), luci::DeadNodeQueryServiceImpl::isDeadNode(), record_minmax::ListFileIterator::ListFileIterator(), record_minmax::RecordMinMax::profileData(), record_minmax::RecordMinMax::profileDataInParallel(), record_minmax::RandomIterator::RandomIterator(), mpqsolver::bisection::BisectionSolver::run(), luci::DynamicBatchToSingleBatchPass::run(), prunner::PModelsRunner::run(), dalgona::Dalgona::runAnalysisWithH5Input(), dalgona::Dalgona::runAnalysisWithRandomInput(), dalgona::PythonHooks::startNetworkExecution(), and InterpreterWrapper::writeInputTensor().

INSTANTIATE() [1/2]

loco::INSTANTIATE

(

DataType::FLOAT32

)

INSTANTIATE() [2/2]

loco::INSTANTIATE

(

DataType::S32

)

link() [1/2]

void loco::link	(	GraphInput *	input,
		Pull *	pull
	)

Definition at line 160 of file Nodes.cpp.

{ pull->index(input->index()); }

References loco::Pull::index().

link() [2/2]

void loco::link	(	GraphOutput *	output,
		Push *	push
	)

Definition at line 65 of file Nodes.cpp.

{ push->index(output->index()); }

References loco::Push::index().

Referenced by GraphTestcase< GraphCode::AvgPool2D >::GraphTestcase(), GraphTestcase< GraphCode::MaxPool2D >::GraphTestcase(), GraphTestcase< GraphCode::TensorConcat >::GraphTestcase(), GraphTestcase< GraphCode::TensorTranspose >::GraphTestcase(), InputLayer::operator()(), OutputLayer::operator()(), exo::test::TestGraph::TestGraph(), mir2loco::Transformer::visit(), and mir2loco::Transformer::visit().

make_dimension()

Dimension loco::make_dimension

(

void

)

Make an "unknown" dimension.

Definition at line 30 of file Dimension.cpp.

{ return Dimension{}; }

make_graph()

std::unique_ptr< Graph > loco::make_graph

(

void

)

Definition at line 131 of file Graph.cpp.

{ return std::unique_ptr<Graph>{new Graph}; }

Referenced by GraphTestcase< GraphCode::ConstGen >::GraphTestcase(), GraphTestcase< GraphCode::Relu >::GraphTestcase(), GraphTestcase< GraphCode::FixedReshape >::GraphTestcase(), GraphTestcase< GraphCode::TensorBroadcast >::GraphTestcase(), moco::Importer::import(), moco::onnx::Frontend::load(), luci::produce_pmodules(), exo::test::TestGraph::TestGraph(), luci::test::TestGraph::TestGraph(), and mir2loco::Transformer::transform().

must_cast() [1/8]

template<typename T >

T loco::must_cast

(

const FeatureDecoder *

node

)

Definition at line 95 of file FeatureCodec.h.

{
  return _must_cast<T, const FeatureDecoder *>(node);
}

must_cast() [2/8]

template<typename T >

T loco::must_cast

(

const FeatureEncoder *

node

)

Definition at line 85 of file FeatureCodec.h.

{
  return _must_cast<T, const FeatureEncoder *>(node);
}

must_cast() [3/8]

template<typename T >

T loco::must_cast

(

const FilterEncoder *

node

)

Definition at line 69 of file FilterCodec.h.

{
  return _must_cast<T, const FilterEncoder *>(node);
}

must_cast() [4/8]

template<typename T >

T loco::must_cast

(

const Node *

node

)

Definition at line 151 of file Node.h.

{ return _must_cast<T, const Node *>(node); }

must_cast() [5/8]

template<typename T >

T loco::must_cast

(

FeatureDecoder *

node

)

Definition at line 90 of file FeatureCodec.h.

{
  return _must_cast<T, FeatureDecoder *>(node);
}

must_cast() [6/8]

template<typename T >

T loco::must_cast

(

FeatureEncoder *

node

)

A helper dynamic_cast that throws when failed.

Definition at line 80 of file FeatureCodec.h.

{
  return _must_cast<T, FeatureEncoder *>(node);
}

must_cast() [7/8]

template<typename T >

T loco::must_cast

(

FilterEncoder *

node

)

A helper dynamic_cast that throws when failed.

Definition at line 64 of file FilterCodec.h.

{
  return _must_cast<T, FilterEncoder *>(node);
}

must_cast() [8/8]

template<typename T >

T loco::must_cast

(

Node *

node

)

A helper dynamic_cast that throws when failed.

Definition at line 149 of file Node.h.

{ return _must_cast<T, Node *>(node); }

operator<<() [1/5]

std::ostream & loco::operator<<	(	std::ostream &	os,
		const loco::Dimension &	d
	)

Definition at line 22 of file FormattedTensorShape.cpp.

{
  os << (d.known() ? std::to_string(d.value()) : std::string{"?"});
  return os;
}

References loco::Dimension::known(), and loco::Dimension::value().

operator<<() [2/5]

std::ostream & loco::operator<<	(	std::ostream &	os,
		const loco::FeatureShape &	feature_shape
	)

dump FeatureShape values to stream

Definition at line 22 of file LogHelper.cpp.

{
  os << "[" << feature_shape.count().value() << "," << feature_shape.height().value() << ","
     << feature_shape.width().value() << "," << feature_shape.depth().value() << "]";
  return os;
}

References loco::FeatureShape::count(), loco::FeatureShape::depth(), loco::FeatureShape::height(), loco::Dimension::value(), and loco::FeatureShape::width().

operator<<() [3/5]

std::ostream & loco::operator<<	(	std::ostream &	os,
		const loco::FilterShape &	filter_shape
	)

dump FilterShape values to stream

Definition at line 29 of file LogHelper.cpp.

{
  os << "[" << filter_shape.height().value() << "," << filter_shape.width().value() << ","
     << filter_shape.depth().value() << "," << filter_shape.count().value() << "]";
  return os;
}

References loco::FilterShape::count(), loco::FilterShape::depth(), loco::FilterShape::height(), loco::Dimension::value(), and loco::FilterShape::width().

operator<<() [4/5]

std::ostream & loco::operator<<	(	std::ostream &	os,
		const loco::Padding2D &	pad
	)

dump Padding2D values to stream

Definition at line 49 of file LogHelper.cpp.

{
  os << "[TLBR " << pad.top() << "," << pad.left() << "," << pad.bottom() << "," << pad.right()
     << "]";
 
  return os;
}

References loco::Padding2D::bottom(), loco::Padding2D::left(), loco::Padding2D::right(), and loco::Padding2D::top().

operator<<() [5/5]

std::ostream & loco::operator<<	(	std::ostream &	os,
		const loco::TensorShape &	tensor_shape
	)

dump TensorShape values to stream

Definition at line 36 of file LogHelper.cpp.

{
  os << "[";
  for (uint32_t r = 0; r < tensor_shape.rank(); ++r)
  {
    if (r)
      os << ",";
    os << tensor_shape.dim(r).value();
  }
  os << "]";
  return os;
}

References loco::TensorShape::dim(), loco::TensorShape::rank(), and loco::Dimension::value().

operator==() [1/5]

bool loco::operator==	(	const Dimension &	lhs,
		const Dimension &	rhs
	)

Equality operator between two Dimensions.

Note

Refer to the definition of equality of dimemsion at https://www.tensorflow.org/api_docs/python/tf/Dimension#__eq__

Definition at line 22 of file Dimension.cpp.

{
  return lhs.known() && rhs.known() && lhs.value() == rhs.value();
}

References loco::Dimension::known(), and loco::Dimension::value().

operator==() [2/5]

bool loco::operator==	(	const Dimension &	lhs,
		uint32_t	rhs
	)

Definition at line 27 of file Dimension.cpp.

{ return lhs.known() && lhs.value() == rhs; }

References loco::Dimension::known(), and loco::Dimension::value().

operator==() [3/5]

bool loco::operator==	(	const NodeShape &	lhs,
		const NodeShape &	rhs
	)

Definition at line 214 of file NodeShape.cpp.

{
  if (lhs.domain() != rhs.domain())
    return false;
 
  switch (lhs.domain())
  {
    case loco::Domain::Tensor:
    {
      auto lhs_t = lhs.as<TensorShape>();
      auto rhs_t = rhs.as<TensorShape>();
      if (lhs_t.rank() != rhs_t.rank())
        return false;
      for (uint32_t axis = 0; axis < lhs_t.rank(); ++axis)
      {
        if (!(lhs_t.dim(axis) == rhs_t.dim(axis)))
          return false;
      }
      return true;
    }
 
    case loco::Domain::Feature:
    {
      auto lhs_f = lhs.as<FeatureShape>();
      auto rhs_f = rhs.as<FeatureShape>();
 
      return (lhs_f.count() == rhs_f.count() && lhs_f.depth() == rhs_f.depth() &&
              lhs_f.height() == rhs_f.height() && lhs_f.width() == rhs_f.width());
    }
 
    case loco::Domain::Filter:
    {
      auto lhs_f = lhs.as<FilterShape>();
      auto rhs_f = rhs.as<FilterShape>();
 
      return (lhs_f.count() == rhs_f.count() && lhs_f.depth() == rhs_f.depth() &&
              lhs_f.height() == rhs_f.height() && lhs_f.width() == rhs_f.width());
    }
 
    case loco::Domain::DepthwiseFilter:
    {
      auto lhs_f = lhs.as<DepthwiseFilterShape>();
      auto rhs_f = rhs.as<DepthwiseFilterShape>();
 
      return (lhs_f.multiplier() == rhs_f.multiplier() && lhs_f.depth() == rhs_f.depth() &&
              lhs_f.height() == rhs_f.height() && lhs_f.width() == rhs_f.width());
    }
 
    case loco::Domain::Bias:
    {
      auto lhs_f = lhs.as<BiasShape>();
      auto rhs_f = rhs.as<BiasShape>();
 
      return (lhs_f.length() == rhs_f.length());
    }
 
    case loco::Domain::Matrix:
    {
      auto lhs_f = lhs.as<MatrixShape>();
      auto rhs_f = rhs.as<MatrixShape>();
 
      return (lhs_f.height() == rhs_f.height() && lhs_f.width() == rhs_f.width());
    }
 
    default:
      throw std::runtime_error("Not supported domain for NodeShape equality");
  }
  return false;
}

References loco::NodeShape::as(), Bias, loco::FeatureShape::count(), loco::FilterShape::count(), loco::DepthwiseFilterShape::depth(), loco::FeatureShape::depth(), loco::FilterShape::depth(), DepthwiseFilter, loco::TensorShape::dim(), loco::NodeShape::domain(), Feature, Filter, loco::DepthwiseFilterShape::height(), loco::FeatureShape::height(), loco::FilterShape::height(), loco::MatrixShape::height(), loco::BiasShape::length(), Matrix, loco::DepthwiseFilterShape::multiplier(), loco::TensorShape::rank(), Tensor, loco::DepthwiseFilterShape::width(), loco::FeatureShape::width(), loco::FilterShape::width(), and loco::MatrixShape::width().

operator==() [4/5]

bool loco::operator==	(	const TensorShape &	lhs,
		const TensorShape &	rhs
	)

'==' operator for TensorShape

Definition at line 44 of file TensorShape.cpp.

{
  if (lhs.rank() != rhs.rank())
    return false;
  for (uint32_t axis = 0; axis < lhs.rank(); ++axis)
  {
    if (!(lhs.dim(axis) == rhs.dim(axis)))
      return false;
  }
  return true;
}

References loco::TensorShape::dim(), and loco::TensorShape::rank().

operator==() [5/5]

bool loco::operator==	(	uint32_t	lhs,
		const Dimension &	rhs
	)

Definition at line 28 of file Dimension.cpp.

{ return rhs.known() && lhs == rhs.value(); }

References loco::Dimension::known(), and loco::Dimension::value().

output_nodes()

std::vector< loco::Node * > loco::output_nodes

(

loco::Graph *

g

)

Definition at line 101 of file Graph.cpp.

{
  std::map<GraphOutputIndex, loco::Node *> table;
 
  for (uint32_t n = 0; n < g->nodes()->size(); ++n)
  {
    auto node = g->nodes()->at(n);
 
    if (auto service = node->dialect()->service<GraphOutputIndexQueryService>())
    {
      if (service->associated(node))
      {
        auto output_index = service->index(node);
        assert(table.find(output_index) == table.end());
        table[output_index] = node;
      }
    }
  }
 
  std::vector<loco::Node *> res;
 
  for (uint32_t n = 0; n < g->outputs()->size(); ++n)
  {
    auto it = table.find(n);
    res.emplace_back(it == table.end() ? nullptr : it->second);
  }
 
  return res;
}

References size.

Referenced by fme_apply::check_patterns_valid(), dalgona::PythonHooks::endNetworkExecution(), entry(), luci::exportNodes(), exo::circle_detail::exportOpDefinedTensors(), exo::tflite_detail::exportOpDefinedTensors(), luci::exportOpDefinedTensors(), fme_detect::EqualizePatternFinder::find(), logo::test::find_first_node_by_type(), moco::test::find_nodes_bytype(), luci::inference_candidates(), circle_eval_diff::MAEPrinter::init(), circle_eval_diff::MSEPrinter::init(), circle_eval_diff::MAPEPrinter::init(), circle_eval_diff::MPEIRPrinter::init(), circle_eval_diff::TopKMatchPrinter::init(), luci_interpreter::GraphLoader::initInputOutputTensors(), luci::DeadNodeQueryServiceImpl::isDeadNode(), luci::layer_info_map(), luci_interpreter::GraphLoader::loadOperators(), InterpreterWrapper::readOutputTensor(), mpqsolver::pattern::Q8LayerNormWithQ16VarianceResolver::resolve(), mpqsolver::pattern::Q8SoftmaxWithQ16SubExpResolver::resolve(), mpqsolver::bisection::BisectionSolver::run(), logo::RemoveDeadNodePass::run(), logo::RemoveDeadNodeWithQueryPass::run(), exo::FoldReshapeOfConstPass::run(), exo::FoldTransposeOfConstPass::run(), exo::FuseBiasAddPass::run(), exo::FuseInstanceNormPass::run(), exo::FuseReluPass::run(), exo::FuseRsqrtPass::run(), exo::FuseSquaredDifferencePass::run(), logo::SimplifyDomainConversionPass::run(), luci::CanonicalizePass::run(), luci::ConvertNCHWToNHWCPass::run(), luci::ConvertToFakeQuantizedModelPass::run(), luci::DecomposeHardSwishPass::run(), luci::DecomposeSoftmaxPass::run(), luci::ExpandBroadcastConstPass::run(), luci::FoldAddV2Pass::run(), luci::FoldCastPass::run(), luci::FoldDensifyPass::run(), luci::FoldDepthwiseConv2DPass::run(), luci::FoldDequantizePass::run(), luci::FoldFullyConnectedPass::run(), luci::FoldGatherPass::run(), luci::FoldMulPass::run(), luci::FoldReshapePass::run(), luci::FoldShapePass::run(), luci::FoldSparseToDensePass::run(), luci::FoldSqueezePass::run(), luci::ForwardReshapeToUnaryOpPass::run(), luci::ForwardTransposeOpPass::run(), luci::FuseActivationFunctionPass::run(), luci::FuseAddToFullyConnectedBiasPass::run(), luci::FuseAddWithConvPass::run(), luci::FuseAddWithFullyConnectedPass::run(), luci::FuseAddWithTConvPass::run(), luci::FuseBatchNormWithConvPass::run(), luci::FuseBatchNormWithDwConvPass::run(), luci::FuseBatchNormWithTConvPass::run(), luci::FuseGeluPass::run(), luci::FuseHorizontalFullyConnectedPass::run(), luci::FuseInstanceNormPass::run(), luci::FuseMeanWithMeanPass::run(), luci::FuseMulToFullyConnectedWeightsPass::run(), luci::FuseMulWithConvPass::run(), luci::FuseMulWithDivPass::run(), luci::FuseMulWithFullyConnectedPass::run(), luci::FusePreActivationBatchNormPass::run(), luci::FusePReluPass::run(), luci::FuseRmsNormPass::run(), luci::FuseRoPEPass::run(), luci::FuseRsqrtPass::run(), luci::FuseSliceWithTConvPass::run(), luci::FuseTransposeWithMeanPass::run(), luci::MakeBatchNormGammaPositivePass::run(), luci::PropagateQParamBackwardPass::run(), luci::PropagateQParamForwardPass::run(), luci::RemoveDuplicateConstPass::run(), luci::RemoveFakeQuantPass::run(), luci::RemoveGatherGuardPass::run(), luci::RemoveQDQForMixedPrecisionOpPass::run(), luci::RemoveQuantDequantSeqPass::run(), luci::RemoveRedundantDequantizePass::run(), luci::RemoveRedundantQuantizePass::run(), luci::RemoveRedundantReshapePass::run(), luci::RemoveRedundantTransposePass::run(), luci::RemoveUnnecessaryAddPass::run(), luci::RemoveUnnecessaryCastPass::run(), luci::RemoveUnnecessaryReshapeNetPass::run(), luci::RemoveUnnecessaryReshapePass::run(), luci::RemoveUnnecessarySlicePass::run(), luci::RemoveUnnecessarySplitPass::run(), luci::RemoveUnnecessaryStridedSlicePass::run(), luci::RemoveUnnecessaryTransposeNetPass::run(), luci::ReplaceMulAddWithDepthwiseConvPass::run(), luci::ReplaceNonConstFCWithBatchMatMulPass::run(), luci::ReplaceSubWithAddPass::run(), luci::ReplaceWithFCGeluFCPass::run(), luci::ResolveCustomOpAddPass::run(), luci::ResolveCustomOpBatchMatMulPass::run(), luci::ResolveCustomOpMatMulPass::run(), luci::ResolveCustomOpMaxPoolWithArgmaxPass::run(), luci::ResolveCustomOpSplitVPass::run(), luci::ResolveFormerCustomOpPass::run(), luci::ShuffleWeightTo16x1Float32Pass::run(), luci::SparsifyTensorPass::run(), luci::SubstituteExpandDimsToReshapePass::run(), luci::SubstitutePackToReshapePass::run(), luci::SubstitutePadV2ToPadPass::run(), luci::SubstituteSplitVToSplitPass::run(), luci::SubstituteSqueezeToReshapePass::run(), luci::SubstituteStridedSliceToReshapePass::run(), luci::SubstituteTransposeToReshapePass::run(), luci::TransformMinMaxToRelu6Pass::run(), luci::TransformMinReluToRelu6Pass::run(), luci::TransformSqrtDivToRsqrtMulPass::run(), luci::UnrollUnidirectionalSequenceLSTMPass::run(), luci::XpSepActFromTransposeConvPass::run(), luci::QuantizeOnnxDequantizeLinearPass::run(), luci::QuantizeOnnxQDQPass::run(), luci::QuantizeWithPredecessorPass::run(), exo::CanonicalNodeConverter< CanonicalType >::run(), exo::TensorBroadcastConverter::run(), exo::MergeConcatNodesPass::run(), fme_apply::InsertScaleShift::run(), fme_apply::FusePostScalePass::run(), fme_apply::FusePreScalePass::run(), luci::CommonSubExpressionEliminationPass::run(), luci::CopyQuantParamPass::run(), luci::ForceQuantParamPass::run(), luci::QuantizeDequantizeWeightsPass::run(), luci::QuantizeDequantizeWeightsWithGPTQPass::run(), luci::QuantizeOnnxFakeQuantModelPass::run(), luci::QuantizeWeightsPass::run(), luci::QuantizeWithMinMaxPass::run(), luci::RequantizePass::run(), luci::QuantizePreCheckerPass::run(), moco::tf::SimpleNodeTransform< ConcreteNode >::run(), logo::ConstantFoldingPass::run(), logo::ResolveDuplicateReshapePass::run(), logo::ResolveRedundantReshapePass::run(), moco::ConstantFoldAdd::run(), moco::ConstantFoldMul::run(), moco::ConstantFoldPack::run(), moco::ConstantFoldStridedSlice::run(), moco::FuseBinaryIntoPreceding::run(), moco::ResolveConstantShape::run(), moco::ResolveFusedBatchNorm::run(), moco::ResolveReshapeWildcardDim::run(), moco::ResolveSquaredDifference::run(), moco::SqueezeReduceNode::run(), luci::FuseBCQPass::run(), prunner::PModelsRunner::run(), prunner::PModelsRunner::save_outputs(), loco::ShapeInferenceSession::to(), loco::TypeInferenceSession::to(), luci::QuantizedModelVerifier::verify(), and q_implant::QImplant::write().

postorder_traversal()

std::vector< loco::Node * > loco::postorder_traversal

(

const std::vector< loco::Node * > &

roots

)

Generate postorder traversal sequence starting from "roots".

HOW TO USE

for (auto node : postorder_traversal(...)) { ... node->do_something() ... }

Definition at line 53 of file Algorithm.cpp.

{
  std::vector<loco::Node *> res;
 
  std::set<loco::Node *> visited_nodes;
  std::stack<Frame> frames;
 
  auto visited = [&visited_nodes](loco::Node *node) {
    return visited_nodes.find(node) != visited_nodes.end();
  };
 
  // NOTE There is not much difference between "auto" and "auto &" as node is of "loco::Node *"
  // type.
  for (auto node : roots)
  {
    assert((node != nullptr) && "root is invalid");
    frames.push(Frame{node});
  }
 
  while (!frames.empty())
  {
    auto &top_frame = frames.top();
 
    if (top_frame.pos() == -1)
    {
      if (visited(top_frame.ptr()))
      {
        frames.pop();
        continue;
      }
      visited_nodes.insert(top_frame.ptr());
    }
 
    top_frame.advance();
 
    assert(top_frame.pos() >= 0);
 
    if (top_frame.pos() < static_cast<int64_t>(top_frame.node().arity()))
    {
      // Let's visit the next argument
      //
      // NOTE "next" may be nullptr if a graph is under construction.
      if (auto next = top_frame.node().arg(top_frame.pos()))
      {
        frames.push(Frame{next});
      }
    }
    else
    {
      // Let's visit the current argument (all the arguments are already visited)
      auto curr = top_frame.ptr();
      res.emplace_back(curr);
      frames.pop();
    }
  }
 
  return res;
}

Referenced by active_nodes(), locop::FormattedGraphImpl< Formatter::LinearV1 >::dump(), luci::exportNodes(), exo::circle_detail::exportOpDefinedTensors(), exo::tflite_detail::exportOpDefinedTensors(), luci::exportOpDefinedTensors(), logo::test::find_first_node_by_type(), locomotiv::Session::infer(), luci::inference_candidates(), luci_interpreter::GraphLoader::loadOperators(), luci::ConvertNCHWToNHWCPass::run(), luci::PropagateQParamBackwardPass::run(), luci::RemoveQDQForMixedPrecisionOpPass::run(), luci::RemoveRedundantQuantizePass::run(), fme_apply::InsertScaleShift::run(), logo::ConstantFoldingPass::run(), logo::ResolveDuplicateReshapePass::run(), logo::ResolveRedundantReshapePass::run(), loco::ShapeInferenceSession::to(), and loco::TypeInferenceSession::to().

preds()

std::set< Node * > loco::preds

(

const Node *

node

)

Enumerate all the predecessors of a given node.

Definition at line 31 of file Node.cpp.

{
  std::set<Node *> res;
 
  for (uint32_t n = 0; n < node->arity(); ++n)
  {
    if (auto pred = node->arg(n))
    {
      res.insert(pred);
    }
  }
 
  return res;
}

References loco::Node::arg(), and loco::Node::arity().

Referenced by luci::fuse_activation_function().

pull_node()

Pull * loco::pull_node	(	Graph *	g,
		const GraphInputIndex &	index
	)

Find a Pull node with a given input index.

Definition at line 162 of file Nodes.cpp.

{
  for (uint32_t n = 0; n < g->nodes()->size(); ++n)
  {
    if (auto pull = dynamic_cast<Pull *>(g->nodes()->at(n)))
    {
      if (pull->indexed() && pull->index() == index)
      {
        return pull;
      }
    }
  }
  return nullptr;
}

References size.

Referenced by exo::circle_detail::registerGraphIOName(), exo::tflite_detail::registerGraphIOName(), and locomotiv::Session::set_input().

push_node()

Push * loco::push_node	(	Graph *	g,
		const GraphOutputIndex &	index
	)

Find a Push node with a given output index.

Definition at line 67 of file Nodes.cpp.

{
  for (uint32_t n = 0; n < g->nodes()->size(); ++n)
  {
    if (auto push = dynamic_cast<Push *>(g->nodes()->at(n)))
    {
      if (push->indexed() && push->index() == index)
      {
        return push;
      }
    }
  }
  return nullptr;
}

References size.

Referenced by exo::circle_detail::registerGraphIOName(), and exo::tflite_detail::registerGraphIOName().

replace()

Subst< SubstQualifier::Default > loco::replace

(

Node *

node

)

Definition at line 82 of file Node.cpp.

{
  // Let's create Subst<SubstQualifier::Default>!
  return Subst<SubstQualifier::Default>{node};
}

Referenced by exo::DomainConverter< CanonicalT, TFLT >::convert(), exo::AvgPool2DConverter::convert(), exo::ConstGenConverter::convert(), exo::Conv2DConverter::convert(), exo::DepthwiseConv2DConverter::convert(), exo::FeatureBiasAddConverter::convert(), exo::MatMulConverter::convert(), exo::MaxPool2DConverter::convert(), exo::TensorConcatConverter::convert(), exo::TensorTransposeConverter::convert(), exo::TransposedConv2DConverter::convert(), luci::fuse_activation_function(), luci::fuse_add_with_conv(), luci::fuse_mul_with_conv(), luci::fuse_sub_with_conv(), logo::ReorderDecodePass< loco::TensorBiasAdd >::run(), logo::ReorderDecodePass< loco::ReLU >::run(), logo::SimplifyDomainConversionPass::run(), luci::FoldDequantizePass::run(), exo::MergeConcatNodesPass::run(), luci::CommonSubExpressionEliminationPass::run(), and luci::swap_mul_add().

shape_erase()

void loco::shape_erase ( Node *  node )

inline

Definition at line 62 of file ShapeInference.h.

{ ShapeInference::erase(node); }

References loco::ShapeInference::erase().

shape_get()

NodeShape loco::shape_get ( const Node *  node )

inline

Definition at line 61 of file ShapeInference.h.

{ return ShapeInference::get(node); }

References loco::ShapeInference::get().

Referenced by moco::tf::BroadcastFunctor::build(), exo::DomainConverter< CanonicalT, TFLT >::convert(), exo::Conv2DConverter::convert(), exo::DepthwiseConv2DConverter::convert(), exo::FeatureBiasAddConverter::convert(), exo::MatMulConverter::convert(), exo::TensorConcatConverter::convert(), exo::TensorTransposeConverter::convert(), exo::TransposedConv2DConverter::convert(), locop::FormattedGraphImpl< Formatter::LinearV1 >::dump(), exo::ShapeInference::get(), locoex::COpShapeInferenceRule::infer(), and moco::node_shape().

shape_known()

bool loco::shape_known ( const Node *  node )

inline

Definition at line 60 of file ShapeInference.h.

{ return ShapeInference::known(node); }

References loco::ShapeInference::known().

Referenced by exo::DomainConverter< CanonicalT, TFLT >::convert(), exo::Conv2DConverter::convert(), exo::DepthwiseConv2DConverter::convert(), exo::MatMulConverter::convert(), exo::TensorConcatConverter::convert(), exo::TransposedConv2DConverter::convert(), locop::FormattedGraphImpl< Formatter::LinearV1 >::dump(), exo::ShapeInference::get(), moco::node_shape(), and loco::ShapeInferenceSession::to().

size()

uint32_t loco::size ( DataType  data_type )

inline

Returns the size of the data type.

Note

If you need the size at compile time, use sizeof(typename DataTypeImpl<DT>::Type).

Definition at line 134 of file DataTypeTraits.h.

{
  switch (data_type)
  {
    case DataType::S4:
      throw std::runtime_error("S4 type is not supported by loco::size");
    case DataType::S8:
      return sizeof(DataTypeImpl<DataType::S8>::Type);
    case DataType::U4:
      throw std::runtime_error("U4 type is not supported by loco::size");
    case DataType::U8:
      return sizeof(DataTypeImpl<DataType::U8>::Type);
    case DataType::S16:
      return sizeof(DataTypeImpl<DataType::S16>::Type);
    case DataType::U16:
      return sizeof(DataTypeImpl<DataType::U16>::Type);
    case DataType::S32:
      return sizeof(DataTypeImpl<DataType::S32>::Type);
    case DataType::U32:
      return sizeof(DataTypeImpl<DataType::U32>::Type);
    case DataType::S64:
      return sizeof(DataTypeImpl<DataType::S64>::Type);
    case DataType::U64:
      return sizeof(DataTypeImpl<DataType::U64>::Type);
    case DataType::FLOAT16:
      return sizeof(DataTypeImpl<DataType::FLOAT16>::Type);
    case DataType::FLOAT32:
      return sizeof(DataTypeImpl<DataType::FLOAT32>::Type);
    case DataType::FLOAT64:
      return sizeof(DataTypeImpl<DataType::FLOAT64>::Type);
    case DataType::BOOL:
      return sizeof(DataTypeImpl<DataType::BOOL>::Type);
    case DataType::STRING:
      // STRING is variable length. Cannot decide size by type
      throw std::runtime_error("Invalid size call with STRING type");
    default:
      // TODO Support remaining data types.
      assert(false);
      return UINT32_MAX; // Avoid compiler warning.
  }
}

Referenced by luci::size().

succs()

std::set< Node * > loco::succs

(

const Node *

node

)

Enumerate all the successors of a given node.

Definition at line 46 of file Node.cpp.

{
  std::set<Node *> res;
 
  for (auto use : node->_uses)
  {
    auto user = use->user();
    assert(user != nullptr);
    res.insert(user);
  }
 
  return res;
}

Referenced by luci_interpreter::collectOutputNodes(), mpqsolver::bisection::compute_depth(), locop::FormattedGraphImpl< Formatter::LinearV1 >::dump(), fme_detect::EqualizePatternFinder::find(), luci::fuse_activation_function(), luci::fuse_add_with_conv(), luci::fuse_mul_with_conv(), luci::fuse_sub_with_conv(), luci::get_channel_dim_index(), exo::test::get_only_succ(), dalgona::outputsPyArray(), record_hessian::HessianObserver::postTensorWrite(), luci::propagate_concat_quantparam(), luci::propagate_pad_v2_quantparam(), logo::ReorderDecodePass< loco::TensorBiasAdd >::run(), logo::ReorderDecodePass< loco::ReLU >::run(), luci::ConvertNCHWToNHWCPass::run(), luci::FoldDequantizePass::run(), opselector::OpSelector::select_by(), and luci::swap_mul_add().

valid()

bool loco::valid	(	Graph *	g,
		std::unique_ptr< ErrorListener > &&	l = nullptr
	)

Validate a loco graph.

"valid" returns true if a given graph has no error.

NOTE Given a valid(non-null) listener, "valid" notifies error details to the listener.

Definition at line 100 of file Verifier.cpp.

{
  class ErrorCounter final : public ErrorListener
  {
  public:
    uint32_t count(void) const { return _count; }
 
  public:
    void notify(const ErrorDetail<ErrorCategory::MissingArgument> &) { _count += 1; }
 
  private:
    uint32_t _count = 0;
  };
 
  ErrorCounter counter;
  graph_verifier(g).enroll(&counter).enroll(std::move(l)).run();
  return counter.count() == 0;
}

Referenced by loco::Permutation< Domain::DepthwiseFilter >::axis(), loco::Permutation< Domain::DepthwiseFilter >::axis(), loco::Permutation< Domain::Feature >::axis(), loco::Permutation< Domain::Feature >::axis(), loco::Permutation< Domain::Filter >::axis(), loco::Permutation< Domain::Filter >::axis(), loco::Permutation< Domain::Matrix >::axis(), loco::Permutation< Domain::Matrix >::axis(), loco::Permutation< Domain::DepthwiseFilter >::mapped(), loco::Permutation< Domain::Feature >::mapped(), loco::Permutation< Domain::Filter >::mapped(), loco::Permutation< Domain::Matrix >::mapped(), loco::PermutingDecoder< Domain::DepthwiseFilter >::shape(), loco::PermutingDecoder< Domain::Feature >::shape(), loco::PermutingDecoder< Domain::Filter >::shape(), loco::PermutingDecoder< Domain::Matrix >::shape(), loco::PermutingEncoder< Domain::Feature >::shape(), loco::PermutingEncoder< Domain::Filter >::shape(), loco::PermutingEncoder< Domain::DepthwiseFilter >::shape(), loco::PermutingEncoder< Domain::Matrix >::shape(), mir2loco::Transformer::transform(), luci::validate(), luci::validate_shape(), loco::PermutingEncoder< Domain::DepthwiseFilter >::value(), loco::PermutingEncoder< Domain::Feature >::value(), loco::PermutingEncoder< Domain::Filter >::value(), loco::PermutingEncoder< Domain::Matrix >::value(), loco::PermutingDecoder< Domain::Feature >::value(), loco::PermutingDecoder< Domain::Filter >::value(), loco::PermutingDecoder< Domain::DepthwiseFilter >::value(), and loco::PermutingDecoder< Domain::Matrix >::value().