tvm.ir

Common data structures across all IR variants.

Classes

Array

Array container of TVM.

Attrs

Attribute node, which is mainly use for defining attributes of relay operators.

BaseExpr

Base class of all the expressions.

BaseFunc

Base class of all functions.

CallingConv

Possible kinds of calling conventions.

Constructor(name_hint, inputs, belong_to)

Relay ADT constructor.

DictAttrs

Dictionary attributes.

EnvFunc

Environment function.

FuncType(arg_types, ret_type[, type_params, …])

Function type.

GlobalTypeVar(name_hint[, kind])

A global type variable that is used for defining new types or type aliases.

GlobalVar(name_hint)

A global variable in the IR.

IRModule([functions, type_definitions])

IRModule that holds functions and type definitions.

IncompleteType([kind])

Incomplete type during type inference.

Map

Map container of TVM.

Node

Base class of all IR Nodes, implements astext function.

Op()

Primitive operator in the IR.

PointerType(element_type)

PointerType used in the low-level TIR.

PrimExpr

Base class of all primitive expressions.

PrimType(dtype)

Primitive data type in the low level IR

Range(begin[, end])

Represent a range in TVM.

RelayExpr

Base class of all non-primitive expressions.

RelayRefType(value)

Reference Type in relay.

SourceName(name)

A identifier for a source location.

Span(source, lineno, col_offset)

Specifies a location in a source program.

TensorType(shape[, dtype])

A concrete TensorType in Relay.

TupleType(fields)

The type of tuple values.

Type

The base class of all types.

TypeCall(func, args)

Type function application.

TypeConstraint

Abstract class representing a type constraint.

TypeData(header, type_vars, constructors)

Stores the definition for an Algebraic Data Type (ADT) in Relay.

TypeKind

Possible kinds of TypeVars.

TypeRelation(func, args, num_inputs, attrs)

User defined type relation, it is an input-output relation on types.

TypeVar(name_hint[, kind])

Type parameter in functions.

Functions

assert_structural_equal(lhs, rhs[, …])

Assert lhs and rhs are structurally equal to each other.

load_json(json_str)

Load tvm object from json_str.

make_node(type_key, **kwargs)

Make a new IR node by its type key and fields

register_op_attr(op_name, attr_key[, value, …])

Register an operator property of an operator by name.

save_json(node)

Save tvm object as json string.

structural_equal(lhs, rhs[, map_free_vars])

Check structural equality of lhs and rhs.

structural_hash(node[, map_free_vars])

Compute structural hash of node

class tvm.ir.SourceName(name)

A identifier for a source location.

Parameters

name (str) – The name of the source.

class tvm.ir.Span(source, lineno, col_offset)

Specifies a location in a source program.

Parameters
  • source (SourceName) – The source name.

  • lineno (int) – The line number.

  • col_offset (int) – The column offset of the location.

class tvm.ir.Node

Base class of all IR Nodes, implements astext function.

Methods

astext([show_meta_data, annotate])

Get the text format of the expression.

astext(show_meta_data=True, annotate=None)

Get the text format of the expression.

Parameters
  • show_meta_data (bool) – Whether to include meta data section in the text if there is meta data.

  • annotate (Optional[Object->str]) – Optionally annotate function to provide additional information in the comment block.

Returns

text – The text format of the expression.

Return type

str

Notes

The meta data section is necessary to fully parse the text format. However, it can contain dumps that are big (e.g constant weights), so it can be helpful to skip printing the meta data section.

class tvm.ir.EnvFunc

Environment function.

This is a global function object that can be serialized by its name.

Methods

get(name)

Get a static env function

static get(name)

Get a static env function

Parameters

name (str) – The name of the function.

tvm.ir.load_json(json_str)

Load tvm object from json_str.

Parameters

json_str (str) – The json string

Returns

node – The loaded tvm node.

Return type

Object

tvm.ir.save_json(node)

Save tvm object as json string.

Parameters

node (Object) – A TVM object to be saved.

Returns

json_str – Saved json string.

Return type

str

tvm.ir.structural_equal(lhs, rhs, map_free_vars=False)

Check structural equality of lhs and rhs.

The structural equality is recursively defined in the DAG of IRNodes. There are two kinds of nodes:

  • Graph node: a graph node in lhs can only be mapped as equal to one and only one graph node in rhs.

  • Normal node: equality is recursively defined without the restriction of graph nodes.

Vars(tir::Var, TypeVar) and non-constant relay expression nodes are graph nodes. For example, it means that %1 = %x + %y; %1 + %1 is not structurally equal to %1 = %x + %y; %2 = %x + %y; %1 + %2 in relay.

A var-type node(e.g. tir::Var, TypeVar) can be mapped as equal to another var with the same type if one of the following condition holds:

  • They appear in a same definition point(e.g. function argument).

  • They points to the same VarNode via the same_as relation.

  • They appear in a same usage point, and map_free_vars is set to be True.

The rules for var are used to remap variables occurs in function arguments and let-bindings.

Parameters
  • lhs (Object) – The left operand.

  • rhs (Object) – The left operand.

  • map_free_vars (bool) – Whether or not shall we map free vars that does not bound to any definitions as equal to each other.

Returns

result – The comparison result.

Return type

bool

See also

structural_hash(), assert_strucural_equal()

tvm.ir.assert_structural_equal(lhs, rhs, map_free_vars=False)

Assert lhs and rhs are structurally equal to each other.

Parameters
  • lhs (Object) – The left operand.

  • rhs (Object) – The left operand.

  • map_free_vars (bool) – Whether or not shall we map free vars that does not bound to any definitions as equal to each other.

:raises ValueError : if assertion does not hold.:

tvm.ir.structural_hash(node, map_free_vars=False)

Compute structural hash of node

The structural hash value is recursively defined in the DAG of IRNodes. There are two kinds of nodes:

  • Normal node: the hash value is defined by its content and type only.

  • Graph node: each graph node will be assigned a unique index ordered by the first occurence during the visit. The hash value of a graph node is combined from the hash values of its contents and the index.

structural_hash is made to be concistent with structural_equal. If two nodes are structurally equal to each other, then their structural hash (with the same map_free_vars option) should be equal to each other as well.

If the structural hash of two nodes equals to each other, then it is highly likely(except for rare hash value collison cases) that the two nodes are structurally equal to each other.

Parameters
  • node (Object) – The input to be hashed.

  • map_free_vars (bool) – If map_free_vars is set to true, we will hash free variables by the order of their occurences. Otherwise, we will hash by their in-memory pointer address.

Returns

result – The hash result

Return type

int

See also

structrual_equal()

class tvm.ir.Type

The base class of all types.

Methods

same_as(other)

Compares two Relay types by referential equality.

same_as(other)

Compares two Relay types by referential equality.

class tvm.ir.TypeKind

Possible kinds of TypeVars.

class tvm.ir.PrimType(dtype)

Primitive data type in the low level IR

Parameters

dtype (str) – The runtime data type relates to the primtype.

class tvm.ir.PointerType(element_type)

PointerType used in the low-level TIR.

Parameters

element_type (tvm.ir.Type) – The type of pointer’s element.

class tvm.ir.TypeVar(name_hint, kind=<TypeKind.Type: 0>)

Type parameter in functions.

A type variable represents a type placeholder which will be filled in later on. This allows the user to write functions which are generic over types.

Parameters
  • name_hint (str) – The name of the type variable. This name only acts as a hint, and is not used for equality.

  • kind (Optional[TypeKind]) – The kind of the type parameter.

class tvm.ir.GlobalTypeVar(name_hint, kind=<TypeKind.AdtHandle: 5>)

A global type variable that is used for defining new types or type aliases.

Parameters
  • name_hint (str) – The name of the type variable. This name only acts as a hint, and is not used for equality.

  • kind (Optional[TypeKind]) – The kind of the type parameter.

class tvm.ir.TupleType(fields)

The type of tuple values.

Parameters

fields (List[Type]) – The fields in the tuple

class tvm.ir.TypeConstraint

Abstract class representing a type constraint.

class tvm.ir.FuncType(arg_types, ret_type, type_params=None, type_constraints=None)

Function type.

A function type consists of a list of type parameters to enable the definition of generic functions, a set of type constraints which we omit for the time being, a sequence of argument types, and a return type.

We can informally write them as: forall (type_params), (arg_types) -> ret_type where type_constraints

Parameters
  • arg_types (List[tvm.relay.Type]) – The argument types

  • ret_type (tvm.relay.Type) – The return type.

  • type_params (Optional[List[tvm.relay.TypeVar]]) – The type parameters

  • type_constraints (Optional[List[tvm.relay.TypeConstraint]]) – The type constraints.

class tvm.ir.IncompleteType(kind=<TypeKind.Type: 0>)

Incomplete type during type inference.

kindOptional[TypeKind]

The kind of the incomplete type.

class tvm.ir.RelayRefType(value)

Reference Type in relay.

Parameters

value (Type) – The value type.

class tvm.ir.TensorType(shape, dtype='float32')

A concrete TensorType in Relay.

This is the type assigned to tensors with a known dtype and shape. For example, a tensor of float32 and (5, 5).

Parameters

Attributes

concrete_shape

Get shape of the type as concrete tuple of int.

property concrete_shape

Get shape of the type as concrete tuple of int.

Returns

shape – The concrete shape of the Type.

Return type

List[int]

:raises TypeError : If the shape is symbolic:

class tvm.ir.TypeCall(func, args)

Type function application.

Parameters
Returns

type_call – The type function application.

Return type

TypeCall

class tvm.ir.TypeRelation(func, args, num_inputs, attrs)

User defined type relation, it is an input-output relation on types.

TypeRelation is more generalized than TypeCall as it allows inference

of both inputs and outputs.

Parameters
  • func (EnvFunc) – User defined relation function.

  • args ([tvm.ir.Type]) – List of types to the func.

  • num_inputs (int) – Number of input arguments in args, this act as a hint for type inference.

  • attrs (Attrs) – The attribute attached to the relation information

Returns

type_relation – The type relation.

Return type

tvm.ir.TypeRelation

class tvm.ir.BaseExpr

Base class of all the expressions.

class tvm.ir.PrimExpr

Base class of all primitive expressions.

PrimExpr is used in the low-level code optimizations and integer analysis.

class tvm.ir.RelayExpr

Base class of all non-primitive expressions.

Attributes

checked_type

Get the checked type of tvm.relay.Expr.

property checked_type

Get the checked type of tvm.relay.Expr.

Returns

checked_type – The checked type.

Return type

tvm.relay.Type

class tvm.ir.GlobalVar(name_hint)

A global variable in the IR.

GlobalVar is used to refer to the global functions stored in the IRModule.

Parameters

name_hint (str) – The name of the variable.

class tvm.ir.Range(begin, end=None)

Represent a range in TVM.

You do not need to create a Range explicitly. Python lists and tuples will be converted automatically to a Range in API functions.

Parameters
  • begin (PrimExpr) – The begin value of the range when end is None. Otherwise it is the length of the range.

  • end (Optional[PrimExpr]) – The end value of the range.

Methods

make_by_min_extent(min_value, extent)

Construct a Range by min and extent.

Note

The constructor creates the range [begin, end) if the end argument is not None. Otherwise, it creates [0, begin).

static make_by_min_extent(min_value, extent)

Construct a Range by min and extent.

This constructs a range in [min_value, min_value + extent)

Parameters
  • min_value (PrimExpr) – The minimum value of the range.

  • extent (PrimExpr) – The extent of the range.

Returns

rng – The constructed range.

Return type

Range

class tvm.ir.Op

Primitive operator in the IR.

Methods

get(op_name)

Get the Op for a given name

get_attr(attr_name)

Get additional attribute about the operator.

reset_attr(attr_name)

Reset attribute about the operator.

set_attr(attr_name, value[, plevel])

Set attribute about the operator.

static get(op_name)

Get the Op for a given name

Parameters

op_name (str) – The operator name

Returns

op – The op of the corresponding name

Return type

Op

get_attr(attr_name)

Get additional attribute about the operator.

Parameters

attr_name (str) – The attribute name.

Returns

value – The attribute value

Return type

object

set_attr(attr_name, value, plevel=10)

Set attribute about the operator.

Parameters
  • attr_name (str) – The attribute name

  • value (object) – The attribute value

  • plevel (int) – The priority level

reset_attr(attr_name)

Reset attribute about the operator.

Parameters

attr_name (str) – The attribute name

tvm.ir.register_op_attr(op_name, attr_key, value=None, level=10)

Register an operator property of an operator by name.

Parameters
  • op_name (str) – The name of operator

  • attr_key (str) – The attribute name.

  • value (object, optional) – The value to set

  • level (int, optional) – The priority level

Returns

fregister – Register function if value is not specified.

Return type

function

class tvm.ir.CallingConv

Possible kinds of calling conventions.

class tvm.ir.BaseFunc

Base class of all functions.

Attributes

attrs

Return the attrs member of the function.

Methods

with_attr(attr_key_or_dict[, attr_value])

Create a new copy of the function and update the attribute.

property attrs

Return the attrs member of the function.

with_attr(attr_key_or_dict, attr_value=None)

Create a new copy of the function and update the attribute.

Parameters
  • attr_key_or_dict (Union[str, dict]) – The attribute key to use or a dict containing multiple key value pairs.

  • attr_value (Object) – The new attribute value.

Returns

func – A new copy of the function

Return type

Function

class tvm.ir.Constructor(name_hint, inputs, belong_to)

Relay ADT constructor.

Parameters
  • name_hint (str) – Name of constructor (only a hint).

  • inputs (List[Type]) – Input types.

  • belong_to (GlobalTypeVar) – Denotes which ADT the constructor belongs to.

class tvm.ir.TypeData(header, type_vars, constructors)

Stores the definition for an Algebraic Data Type (ADT) in Relay.

Note that ADT definitions are treated as type-level functions because the type parameters need to be given for an instance of the ADT. Thus, any global type var that is an ADT header needs to be wrapped in a type call that passes in the type params.

Parameters
  • header (GlobalTypeVar) – The name of the ADT. ADTs with the same constructors but different names are treated as different types.

  • type_vars (List[TypeVar]) – Type variables that appear in constructors.

  • constructors (List[Constructor]) – The constructors for the ADT.

class tvm.ir.IRModule(functions=None, type_definitions=None)

IRModule that holds functions and type definitions.

IRModule is the basic unit for all IR transformations across the stack.

Parameters

functions (Optional[dict]) – Map of global var to BaseFunc

Methods

from_expr(expr[, functions, type_defs])

Construct a module from a standalone expression.

get_constructor(tag)

Look up an ADT constructor by tag.

get_global_type_var(name)

Get a global type variable in the function by name.

get_global_type_vars()

Collect all global type vars defined in this module.

get_global_var(name)

Get a global variable in the function by name.

get_global_vars()

Collect all global vars defined in this module.

update(other)

Insert functions in another Module to current one.

update(other)

Insert functions in another Module to current one.

Parameters

other (IRModule) – The module to merge into the current Module.

get_global_var(name)

Get a global variable in the function by name.

Parameters

name (str) – The name of the global variable.

Returns

global_var – The global variable mapped to name.

Return type

GlobalVar

Raises

tvm.error.TVMError if we cannot find corresponding global var.

get_global_vars()

Collect all global vars defined in this module.

Returns

global_vars – An array of global vars.

Return type

Array[GlobalVar]

get_global_type_vars()

Collect all global type vars defined in this module.

Returns

global_type_vars – An array of global type vars.

Return type

Array[GlobalTypeVar]

get_global_type_var(name)

Get a global type variable in the function by name.

Parameters

name (str) – The name of the global type variable.

Returns

global_type_var – The global variable mapped to name.

Return type

GlobalTypeVar

Raises

tvm.error.TVMError if we cannot find corresponding global type var.

get_constructor(tag)

Look up an ADT constructor by tag.

Parameters

tag (int) – The tag for a constructor.

Returns

constructor – The constructor associated with the given tag,

Return type

Constructor

Raises

tvm.error.TVMError if the corresponding constructor cannot be found.

static from_expr(expr, functions=None, type_defs=None)

Construct a module from a standalone expression.

Parameters
  • expr (RelayExpr) – The starting expression

  • global_funcs (Optional[dict]) – Map of global vars to function definitions

  • type_defs (Optional[dict]) – Map of global type vars to type definitions

Returns

mod – A module containing the passed definitions, where expr is set as the entry point (wrapped in a function if necessary)

Return type

Module

class tvm.ir.Attrs

Attribute node, which is mainly use for defining attributes of relay operators.

Used by function registered in python side, such as compute, schedule and alter_layout. Attrs is passed as the first argument to these functions.

Methods

get_int(key)

Get a python int value of a key

get_int_tuple(key)

Get a python int tuple of a key

get_str(key)

Get a python int value of a key

keys()

Get list of names in the attribute.

list_field_info()

Get fields information

list_field_info()

Get fields information

Returns

infos – List of field information

Return type

list of AttrFieldInfo

keys()

Get list of names in the attribute.

Returns

keys – List of keys

Return type

list of str

get_int_tuple(key)

Get a python int tuple of a key

Parameters

key (str) –

Returns

value

Return type

Tuple of int

get_int(key)

Get a python int value of a key

Parameters

key (str) –

Returns

value

Return type

int

get_str(key)

Get a python int value of a key

Parameters

key (str) –

Returns

value

Return type

int

class tvm.ir.DictAttrs

Dictionary attributes.

Methods

items()

Get items from the map.

keys()

Get list of names in the attribute.

keys()

Get list of names in the attribute.

Returns

keys – List of keys

Return type

list of str

items()

Get items from the map.

tvm.ir.make_node(type_key, **kwargs)

Make a new IR node by its type key and fields

Parameters
  • type_key (str) – The type key of the node.

  • **kwargs (dict) – The fields of the node.

Returns

node – The corresponding IR Node

Return type

Node

Note

If the created node is instance of AttrsNode, then the creator function will also run bound checks and default value setup as supported by Attrs.

Example

The following code constructs a IntImm object

x = tvm.ir.make_node("IntImm", dtype="int32", value=10)
assert isinstance(x, tvm.tir.IntImm)
assert x.value == 10
class tvm.ir.Array

Array container of TVM.

You do not need to create Array explicitly. Normally python list and tuple will be converted automatically to Array during tvm function call. You may get Array in return values of TVM function call.

class tvm.ir.Map

Map container of TVM.

You do not need to create Map explicitly. Normally python dict will be converted automaticall to Map during tvm function call. You can use convert to create a dict[Object-> Object] into a Map

Methods

get(key[, default])

Get an element with a default value.

items()

Get the items from the map

items()

Get the items from the map

get(key, default=None)

Get an element with a default value.

Parameters
  • key (object) – The attribute key.

  • default (object) – The default object.

Returns

value – The result value.

Return type

object

tvm.transform

Common pass infrastructure across IR variants.

Classes

ModulePass

A pass that works on tvm.IRModule.

Pass

The base class of all passes.

PassContext([opt_level, required_pass, …])

The basis where a Relay optimization/analysis runs on.

PassInfo(opt_level, name[, required])

The class contains the meta data required by a pass.

Sequential([passes, opt_level, name, required])

A pass that works on a sequence of pass objects.

Functions

PrintIR([header, show_meta_data])

A special trace pass that prints the header and IR.

module_pass([pass_func, opt_level, name, …])

Decorate a module pass.

class tvm.transform.ModulePass

A pass that works on tvm.IRModule. Users don’t need to interact with this class directly. Instead, a module pass should be created through module_pass, because the design of the module_pass API is flexible enough to handle the creation of a module pass in different manners. In addition, all members of a module pass can be accessed from the base class. The same rule applies to FunctionPass as well.

class tvm.transform.Pass

The base class of all passes. All methods here are just simple wrappers that are implemented in the backend. They are defined for users to conveniently interact with the base class.

Attributes

info

Get the pass meta.

property info

Get the pass meta.

class tvm.transform.PassContext(opt_level=2, required_pass=None, disabled_pass=None, trace=None, config=None)

The basis where a Relay optimization/analysis runs on. Each pass context contains a number of auxiliary information that is used to help an optimization pass. Such information includes the error reporter to record the errors of during the optimization, etc.

opt_levelOptional[int]

The optimization level of this pass.

required_passOptional[Union[List[str], Set[str], Tuple[str]]]

The list of passes that are required by a certain pass.

disabled_passOptional[Union[List[str], Set[str], Tuple[str]]]

The list of passes that are disabled.

configOptional[Dict[str, Object]]

Additional configurations for specific passes.

Methods

current()

Return the current pass context.

static current()

Return the current pass context.

class tvm.transform.PassInfo(opt_level, name, required=None)

The class contains the meta data required by a pass. It is the container of information needed by running an optimization or analysis. This class can be extended by adding new members when more meta data is needed.

Parameters
  • opt_level (int) – The optimization level of this pass.

  • name (str) – The pass name.

  • required (List[str]) – The list of passes that are required by a certain pass.

tvm.transform.PrintIR(header='', show_meta_data=False)

A special trace pass that prints the header and IR.

Parameters
  • header (str) – The header to be displayed along with the dump.

  • show_meta_data (bool) – A boolean flag to indicate if meta data should be printed.

Returns

Return type

The pass

class tvm.transform.Sequential(passes=None, opt_level=2, name='sequential', required=None)

A pass that works on a sequence of pass objects. Multiple passes can be executed sequentially using this class.

Note that users can also provide a series of passes that they don’t want to apply when running a sequential pass. Pass dependency will be resolved in the backend as well.

Parameters
  • passes (Optional[List[Pass]]) – A sequence of passes candidate for optimization.

  • opt_level (Optional[int]) – The optimization level of this sequential pass. The opt_level of a default sequential pass is set to 0. Note that some of the passes within the Sequantial may still not be executed if their opt_level is higher than the provided opt_level.

  • name (Optional[str]) – The name of the sequential pass.

  • required (Optional[List[str]]) – The list of passes that the sequential pass is dependent on.

tvm.transform.module_pass(pass_func=None, opt_level=None, name=None, required=None)

Decorate a module pass.

This function returns a callback when pass_func is provided. Otherwise, it serves a decorator function.

pass_func can also be a class type with a method transform_module. This function will create a decorated ModulePass using transform_module as the pass function.

Parameters
  • pass_func (Optional[Callable[(Module, PassContext) ->Module]]) – The transformation function or class.

  • opt_level (int) – The optimization level of this module pass.

  • name (Optional[str]) – The name of the module pass. The name could be empty. In this case, the name of the optimization function will be used as the pass name.

  • required (Optional[List[str]]) – The list of passes that the module pass is dependent on.

Returns

create_module_pass – A decorator will be returned if pass_func is not provided, otherwise return the decorated result. The returned decorator has two behaviors depending on the input: A new ModulePass will be returned when we decorate a pass function. A new ModulePass class will be returned when we decorate a class type.

Return type

Union[Callable, ModulePass]

Examples

The following code block decorates a module pass class.

@relay.transform.module_pass
class CustomPipeline:
    def __init__(self, enable_fold):
        self.enable_fold = enable_fold
        self.cse = relay.transform.EliminateCommonSubexpr()
        self.const_fold = relay.transform.FoldConstant()

    def transform_module(self, mod, ctx):
        mod = self.cse(mod, ctx)
        if self.enable_fold:
            mod = self.const_fold(mod, ctx)
        return mod

# create an instance of customized pipeline
pipeline = CustomPipeline(enable_fold=False)
assert isinstance(pipeline, transform.ModulePass)
# run the pipeline.
output_module = pipeline(input_module)

The following code creates a module pass by decorating a user defined transform function.

@relay.transform.module_pass(opt_level=2)
def transform(mod, ctx):
    tp = relay.TensorType((10,), "float32")
    x = relay.var("x", tp)
    gv = relay.GlobalVar("var")
    func = relay.Function([x], relay.abs(x))
    new_mod = tvm.IRModule({gv: func})
    new_mod.update(mod)
    return new_mod

module_pass = transform
assert isinstance(module_pass, transform.ModulePass)
assert module_pass.info.opt_level == 2

# Given a module m, the optimization could be invoked as the follwoing:
updated_mod = module_pass(m)
# Now a function abs should be added to the module m.