#include <treelite_importer.hpp>

Collaboration diagram for ML::experimental::fil::treelite_importer< layout >:

Classes
struct	treelite_node

Public Member Functions
template<typename tl_threshold_t , typename tl_output_t , typename lambda_t >
void	node_for_each (treelite::Tree< tl_threshold_t, tl_output_t > const &tl_tree, lambda_t &&lambda)

template<typename tl_threshold_t , typename tl_output_t , typename iter_t , typename lambda_t >
void	node_transform (treelite::Tree< tl_threshold_t, tl_output_t > const &tl_tree, iter_t output_iter, lambda_t &&lambda)

template<typename tl_threshold_t , typename tl_output_t , typename T , typename lambda_t >
auto	node_accumulate (treelite::Tree< tl_threshold_t, tl_output_t > const &tl_tree, T init, lambda_t &&lambda)

template<typename tl_threshold_t , typename tl_output_t >
auto	get_nodes (treelite::Tree< tl_threshold_t, tl_output_t > const &tl_tree)

template<typename tl_threshold_t , typename tl_output_t >
auto	get_offsets (treelite::Tree< tl_threshold_t, tl_output_t > const &tl_tree)

template<typename lambda_t >
void	tree_for_each (treelite::Model const &tl_model, lambda_t &&lambda)

template<typename iter_t , typename lambda_t >
void	tree_transform (treelite::Model const &tl_model, iter_t output_iter, lambda_t &&lambda)

template<typename T , typename lambda_t >
auto	tree_accumulate (treelite::Model const &tl_model, T init, lambda_t &&lambda)

auto	num_trees (treelite::Model const &tl_model)

auto	get_offsets (treelite::Model const &tl_model)

auto	get_tree_sizes (treelite::Model const &tl_model)

auto	get_num_class (treelite::Model const &tl_model)

auto	get_num_feature (treelite::Model const &tl_model)

auto	get_max_num_categories (treelite::Model const &tl_model)

auto	get_num_categorical_nodes (treelite::Model const &tl_model)

auto	get_num_leaf_vector_nodes (treelite::Model const &tl_model)

auto	get_average_factor (treelite::Model const &tl_model)

auto	get_bias (treelite::Model const &tl_model)

auto	get_postproc_params (treelite::Model const &tl_model)

auto	uses_double_thresholds (treelite::Model const &tl_model)

auto	uses_double_outputs (treelite::Model const &tl_model)

auto	uses_integer_outputs (treelite::Model const &tl_model)

template<index_type variant_index>
auto	import_to_specific_variant (index_type target_variant_index, treelite::Model const &tl_model, index_type num_class, index_type num_feature, index_type max_num_categories, std::vector< std::vector< index_type >> const &offsets, index_type align_bytes=index_type{}, raft_proto::device_type mem_type=raft_proto::device_type::cpu, int device=0, raft_proto::cuda_stream stream=raft_proto::cuda_stream{})

auto	import (treelite::Model const &tl_model, index_type align_bytes=index_type{}, std::optional< bool > use_double_precision=std::nullopt, raft_proto::device_type dev_type=raft_proto::device_type::cpu, int device=0, raft_proto::cuda_stream stream=raft_proto::cuda_stream{})

Detailed Description

template<tree_layout layout>
struct ML::experimental::fil::treelite_importer< layout >

Struct used to import a model from Treelite to FIL

Template Parameters

layout The in-memory layout for nodes to be loaded into FIL

Member Function Documentation

◆ get_average_factor()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_average_factor ( treelite::Model const & tl_model )

inline

◆ get_bias()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_bias ( treelite::Model const & tl_model )

inline

◆ get_max_num_categories()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_max_num_categories ( treelite::Model const & tl_model )

inline

◆ get_nodes()

template<tree_layout layout>

template<typename tl_threshold_t , typename tl_output_t >

auto ML::experimental::fil::treelite_importer< layout >::get_nodes ( treelite::Tree< tl_threshold_t, tl_output_t > const & tl_tree )

inline

◆ get_num_categorical_nodes()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_num_categorical_nodes ( treelite::Model const & tl_model )

inline

◆ get_num_class()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_num_class ( treelite::Model const & tl_model )

inline

◆ get_num_feature()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_num_feature ( treelite::Model const & tl_model )

inline

◆ get_num_leaf_vector_nodes()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_num_leaf_vector_nodes ( treelite::Model const & tl_model )

inline

◆ get_offsets() [1/2]

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_offsets ( treelite::Model const & tl_model )

inline

◆ get_offsets() [2/2]

template<tree_layout layout>

template<typename tl_threshold_t , typename tl_output_t >

auto ML::experimental::fil::treelite_importer< layout >::get_offsets ( treelite::Tree< tl_threshold_t, tl_output_t > const & tl_tree )

inline

◆ get_postproc_params()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_postproc_params ( treelite::Model const & tl_model )

inline

◆ get_tree_sizes()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::get_tree_sizes ( treelite::Model const & tl_model )

inline

◆ import()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::import	(	treelite::Model const &	tl_model,
		index_type	align_bytes = `index_type{}`,
		std::optional< bool >	use_double_precision = `std::nullopt`,
		raft_proto::device_type	dev_type = `raft_proto::device_type::cpu`,
		int	device = `0`,
		raft_proto::cuda_stream	stream = `raft_proto::cuda_stream{}`
	)

inline

Import a treelite model to FIL

Load a model from Treelite to a FIL forest_model. The model will be inspected to determine the correct underlying decision_forest variant to use within the forest_model object.

Parameters

tl_model	The Treelite Model to load
align_bytes	If non-zero, ensure that each tree is stored in a multiple of this value of bytes by padding with empty nodes. This can be useful for increasing the likelihood that successive reads will take place within a single cache line. On GPU, a value of 128 can be used for this purpose. On CPU, a value of either 0 or 64 typically produces optimal performance.
use_double_precision	Whether or not to use 64 bit floats for model evaluation and 64 bit ints for applicable indexing
dev_type	Which device type to use for inference (CPU or GPU)
device	For GPU execution, the device id for the device on which this model is to be loaded
stream	The CUDA stream to use for loading this model (can be omitted for CPU).

◆ import_to_specific_variant()

template<tree_layout layout>

template<index_type variant_index>

auto ML::experimental::fil::treelite_importer< layout >::import_to_specific_variant	(	index_type	target_variant_index,
		treelite::Model const &	tl_model,
		index_type	num_class,
		index_type	num_feature,
		index_type	max_num_categories,
		std::vector< std::vector< index_type >> const &	offsets,
		index_type	align_bytes = `index_type{}`,
		raft_proto::device_type	mem_type = `raft_proto::device_type::cpu`,
		int	device = `0`,
		raft_proto::cuda_stream	stream = `raft_proto::cuda_stream{}`
	)

inline

Assuming that the correct decision_forest variant has been identified, import to that variant

◆ node_accumulate()

template<tree_layout layout>

template<typename tl_threshold_t , typename tl_output_t , typename T , typename lambda_t >

auto ML::experimental::fil::treelite_importer< layout >::node_accumulate	(	treelite::Tree< tl_threshold_t, tl_output_t > const &	tl_tree,
		T	init,
		lambda_t &&	lambda
	)

inline

◆ node_for_each()

template<tree_layout layout>

template<typename tl_threshold_t , typename tl_output_t , typename lambda_t >

void ML::experimental::fil::treelite_importer< layout >::node_for_each	(	treelite::Tree< tl_threshold_t, tl_output_t > const &	tl_tree,
		lambda_t &&	lambda
	)

inline

◆ node_transform()

template<tree_layout layout>

template<typename tl_threshold_t , typename tl_output_t , typename iter_t , typename lambda_t >

void ML::experimental::fil::treelite_importer< layout >::node_transform	(	treelite::Tree< tl_threshold_t, tl_output_t > const &	tl_tree,
		iter_t	output_iter,
		lambda_t &&	lambda
	)

inline

◆ num_trees()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::num_trees ( treelite::Model const & tl_model )

inline

◆ tree_accumulate()

template<tree_layout layout>

template<typename T , typename lambda_t >

auto ML::experimental::fil::treelite_importer< layout >::tree_accumulate	(	treelite::Model const &	tl_model,
		T	init,
		lambda_t &&	lambda
	)

inline

◆ tree_for_each()

template<tree_layout layout>

template<typename lambda_t >

void ML::experimental::fil::treelite_importer< layout >::tree_for_each	(	treelite::Model const &	tl_model,
		lambda_t &&	lambda
	)

inline

◆ tree_transform()

template<tree_layout layout>

template<typename iter_t , typename lambda_t >

void ML::experimental::fil::treelite_importer< layout >::tree_transform	(	treelite::Model const &	tl_model,
		iter_t	output_iter,
		lambda_t &&	lambda
	)

inline

◆ uses_double_outputs()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::uses_double_outputs ( treelite::Model const & tl_model )

inline

◆ uses_double_thresholds()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::uses_double_thresholds ( treelite::Model const & tl_model )

inline

◆ uses_integer_outputs()

template<tree_layout layout>

auto ML::experimental::fil::treelite_importer< layout >::uses_integer_outputs ( treelite::Model const & tl_model )

inline

The documentation for this struct was generated from the following file:

include/cuml/experimental/fil/treelite_importer.hpp

Classes

Public Member Functions

Detailed Description

template<tree_layout layout> struct ML::experimental::fil::treelite_importer< layout >

Member Function Documentation

◆ get_average_factor()

◆ get_bias()

◆ get_max_num_categories()

◆ get_nodes()

◆ get_num_categorical_nodes()

◆ get_num_class()

◆ get_num_feature()

◆ get_num_leaf_vector_nodes()

◆ get_offsets() [1/2]

◆ get_offsets() [2/2]

◆ get_postproc_params()

◆ get_tree_sizes()

◆ import()

◆ import_to_specific_variant()

◆ node_accumulate()

◆ node_for_each()

◆ node_transform()

◆ num_trees()

◆ tree_accumulate()

◆ tree_for_each()

◆ tree_transform()

◆ uses_double_outputs()

◆ uses_double_thresholds()

◆ uses_integer_outputs()

template<tree_layout layout>
struct ML::experimental::fil::treelite_importer< layout >