Reference: Loopy’s Model of a Kernel¶

What Types of Computation can a Loopy Program Express?¶

Loopy programs consist of an a-priori unordered set of statements, operating on $n$-dimensional array variables.

Arrays consist of “plain old data” and structures thereof, as describable by a numpy.dtype. The n-dimensional shape of these arrays is given by a tuple of expressions at most affine in parameters that are fixed for the duration of program execution. Each array variable in the program is either an argument or a temporary variable. A temporary variable is only live within the program, while argument variables are accessible outside the program and constitute the program’s inputs and outputs.

A statement (still called ‘instruction’ in some places, cf. loopy.InstructionBase) encodes an assignment to an entry of an array. The right-hand side of an assignment consists of an expression that may consist of arithmetic operations and calls to functions. If the outermost operation of the RHS expression is a function call, the RHS value may be a tuple, and multiple (still scalar) arrays appear as LHS values. (This is the only sense in which tuple types are supported.) Each statement is parameterized by zero or more loop variables (“inames”). A statement is executed once for each integer point defined by the domain forest for the iname tuple given for that statement (loopy.InstructionBase.within_inames). Each execution of a statement (with specific values of the inames) is called a statement instance. Dependencies between these instances as well as instances of other statements are encoded in the program representation and specify permissible execution orderings. (The semantics of the dependencies are being sharpened.) Assignments (comprising the evaluation of the RHS and the assignment to the LHS) may be specified to be atomic.

The basic building blocks of the domain forest are sets given as conjunctions of equalities and inequalities of quasi-affine expressions on integer tuples, called domains, and represented as instances of islpy.BasicSet. The entries of each integer tuple are either parameters or inames. Each domain may optionally have a parent domain. Parameters of parent-less domains are given by value arguments supplied to the program that will remain unchanged during program execution. Parameters of domains with parents may be

run-time-constant value arguments to the program, or
inames from parent domains, or
scalar, integer temporary variables that are written by statements with iteration domains controlled by a parent domain.

For each tuple of concrete parameter values, the set of iname tuples must be finite. Each iname is defined by exactly one domain.

For a tuple of inames, the domain forest defines an iteration domain by finding all the domains defining the inames involved, along with their parent domains. The resulting tree of domains may contain multiple roots, but no branches. The iteration domain is then constructed by intersecting these domains and constructing the projection of that set onto the space given by the required iname tuple. Observe that, via the parent-child domain mechanism, imperfectly-nested and data-dependent loops become expressible.

The set of functions callable from the language is predefined by the system. Additional functions may be defined by the user by registering them. It is not currently possible to define functions from within Loopy, however work is progressing on permitting this. Even once this is allowed, recursion will not be permitted.

Loop Domain Forest¶

Example:

{ [i]: 0<=i<n }

A kernel’s iteration domain is given by a list of islpy.BasicSet instances (which parametrically represent multi-dimensional sets of tuples of integers). They define the integer values of the loop variables for which instructions (see below) will be executed. It is written in ISL syntax. loopy calls the loop variables inames. In this case, i is the sole iname. The loop domain is given as a conjunction of affine equality and inequality constraints. Integer divisibility constraints (resulting in strides) are also allowed. In the absence of divisibility constraints, the loop domain is convex.

Note that n in the example is not an iname. It is a Domain parameters that is passed to the kernel by the user.

To accommodate some data-dependent control flow, there is not actually a single loop domain, but rather a forest of loop domains (a collection of trees) allowing more deeply nested domains to depend on inames introduced by domains closer to the root.

Here is an example:

{ [l] : 0 <= l <= 2 }
  { [i] : start <= i < end }
  { [j] : start <= j < end }

The i and j domains are “children” of the l domain (visible from indentation). This is also how loopy prints the domain forest, to make the parent/child relationship visible. In the example, the parameters start/end might be read inside of the ‘l’ loop.

The idea is that domains form a forest (a collection of trees), and a “sub-forest” is extracted that covers all the inames for each instruction. Each individual sub-tree is then checked for branching, which is ill-formed. It is declared ill-formed because intersecting, in the above case, the l, i, and j domains could result in restrictions from the i domain affecting the j domain by way of how i affects l–which would be counterintuitive to say the least.)

Inames¶

Loops are (by default) entered exactly once. This is necessary to preserve dependency semantics–otherwise e.g. a fetch could happen inside one loop nest, and then the instruction using that fetch could be inside a wholly different loop nest.

ISL syntax¶

The general syntax of an ISL set is the following:

{[VARIABLES]: CONDITIONS}

VARIABLES is a simple list of identifiers representing loop indices, or, as loopy calls them, inames. Example:

{[i, j, k]: CONDITIONS}

The following constructs are supported for CONDITIONS:

Simple conditions: i <= 15, i>0
Conjunctions: i > 0 and i <= 15
Two-sided conditions: 0 < i <= 15 (equivalent to the previous example)
Identical conditions on multiple variables: 0 < i,j <= 15
Equality constraints: i = j*3 (Note: =, not ==.)
Modulo: i mod 3 = 0
Existential quantifiers: (exists l: i = 3*l) (equivalent to the previous example)

Examples of constructs that are not allowed:

Multiplication by non-constants: j*k
Disjunction: (i=1) or (i=5) (Note: This may be added in a future version of loopy. For now, loop domains have to be convex.)

Domain parameters¶

Domain parameters are identifiers being used in loop domains that are not inames, i.e. they do not define loop variables. In the following domain specification, n is a domain parameter:

{[i,j]: 0 <= i,j < n}

Values of domain parameters arise from

being passed to the kernel as Arguments
being assigned to Temporary Variables to feed into domains lower in the Loop Domain Forest.

Iname Implementation Tags¶

Tag	Meaning
`None` \| `"for"`	Sequential loop
`"ord"`	Forced-order sequential loop
`"l.N"`	Local (intra-group) axis N (“local”)
`"g.N"`	Group-number axis N (“group”)
`"unr"`	Unroll
`"unr_hint"`	Unroll using compiler directives
`"unr_hint.N"`	Unroll at most N times using compiler directives
`"ilp"` \| `"ilp.unr"`	Unroll using instruction-level parallelism
`"ilp.seq"`	Realize parallel iname as innermost loop
`"like.INAME"`	Can be used when tagging inames to tag like another
`"unused.g"` \| `"unused.l"`	Can be to tag as the next unused group/local axis

(Throughout this table, N must be replaced by an actual, zero-based number.)

“ILP” does three things:

Restricts loops to be innermost
Duplicates reduction storage for any reductions nested around ILP usage
Causes a loop (unrolled or not) to be opened/generated for each involved instruction

Identifiers¶

Reserved Identifiers¶

The identifier prefix _lp_ is reserved for internal usage; when creating inames, argument names, temporary variable names, substitution rule names, instruction IDs, and other identifiers, users should not use names beginning with _lp_. This prefix is used for identifiers created internally when operating on Loopy’s kernel IR. For Loopy developers, further information on name prefixes used within submodules is below.

Identifier Registry¶

Functionality in loopy must use identifiers beginning with _lp_ for all internally-created identifiers. Additionally, each name beginning with _lp_ must start with one of the reserved prefixes below. New prefixes may be registered by adding them to the table below. New prefixes may not themselves be the prefix of an existing prefix.

Reserved Identifier Prefixes

Reserved Prefix	Usage (module or purpose)
`_lp_linchk_`	`loopy.linearization.checker`

Note

Existing Loopy code may not yet fully satisfy these naming requirements. Name changes are in progress, and prefixes will be added to this registry as they are created.

Instructions¶

class loopy.HappensAfter(variable_name: str | None, instances_rel: Map | None)[source]¶

A class representing a “happens-after” relationship between two statements found in a loopy.LoopKernel. Used to validate that a given kernel transformation respects the data dependencies in a given program.

variable_name¶: The name of the variable responsible for the dependency. For backward compatibility purposes, this may be None. In this case, the dependency semantics revert to the deprecated, statement-level dependencies of prior versions of loopy.

instances_rel¶

An islpy.Map representing the precise happens-after relationship. The domain and range are sets of statement instances. The instances in the domain are required to execute before the instances in the range.

Map dimensions are named according to the order of appearance of the inames in a loopy program. The dimension names in the range are appended with a prime to signify that the mapped instances are distinct.

As a (deprecated) matter of backward compatibility, this may be None, in which case the semantics revert to the (underspecified) statement-level dependencies of prior versions of loopy.

A base class for all types of instruction that can occur in a kernel.

id¶: An (otherwise meaningless) identifier that is unique within a loopy.LoopKernel.

Instruction ordering

depends_on¶

a frozenset of id values of InstructionBase instances that must be executed before this one. Note that loopy.preprocess_kernel() (usually invoked automatically) augments this by adding dependencies on any writes to temporaries read by this instruction.

May be None to invoke the default.

There are two extensions to this:

You may use * as a wildcard in the given IDs. This will be expanded to all matching instruction IDs during loopy.make_kernel().
Instead of an instruction ID, you may pass an instance of loopy.match.MatchExpressionBase into the depends_on frozenset. The given expression will be used to add any matching instructions in the kernel to depends_on during loopy.make_kernel(). Note, that this is not meant as a user-facing interface.

depends_on_is_final¶

A bool determining whether depends_on constitutes the entire list of iname dependencies. If not marked final, various semi-broken heuristics will try to add further dependencies.

Defaults to False.

groups¶: A frozenset of strings indicating the names of ‘instruction groups’ of which this instruction is a part. An instruction group is considered ‘active’ as long as one (but not all) instructions of the group have been executed.

conflicts_with_groups¶: A frozenset of strings indicating which instruction groups (see groups) may not be active when this instruction is scheduled.

priority¶: Scheduling priority, an integer. Higher means ‘execute sooner’. Default 0.

Synchronization

no_sync_with¶

a frozenset of tuples of the form (insn_id, scope), where insn_id refers to id of InstructionBase instances and scope is one of the following strings:

“local”

“global”

“any”.

An element (insn_id, scope) means “do not consider any variable access conflicting for variables of scope between this instruction and insn_id”. Specifically, loopy will not complain even if it detects that accesses potentially requiring ordering (e.g. by dependencies) exist, and it will not emit barriers to guard any dependencies from this instruction on insn_id that may exist.

Note, that no_sync_with allows instruction matching through wildcards and match expression, just like depends_on.

This data is used specifically by barrier insertion and loopy.check.check_variable_access_ordered().

Conditionals

predicates¶: a frozenset of expressions. The conjunction (logical and) of their truth values (as defined by C) determines whether this instruction should be run.

Iname dependencies

within_inames¶: A frozenset of inames identifying the loops within which this instruction will be executed.

Iname dependencies

Tagging

tags¶: A frozenset of subclasses of pytools.tag.Tag used to provide metadata on this object. Legacy string tags are converted to LegacyStringInstructionTag or, if they used to carry a functional meaning, the tag carrying that same functional meaning (e.g. UseStreamingStoreTag).

__init__(id: str | None, happens_after: Mapping[str, HappensAfter] | frozenset[str] | str | None, depends_on_is_final: bool | None, groups: frozenset[str] | None, conflicts_with_groups: frozenset[str] | None, no_sync_with: frozenset[tuple[str, str]] | None, within_inames_is_final: bool | None, within_inames: frozenset[str] | None, priority: int | None, predicates: frozenset[str] | None, tags: frozenset[Tag] | None, *, depends_on: frozenset[str] | str | None = None) → None[source]¶

assignee_var_names()[source]¶: Return a tuple of assignee variable names, one for each quantity being assigned to.

assignee_subscript_deps()[source]¶: Return a list of sets of variable names referred to in the subscripts of the quantities being assigned to, one for each assignee.

with_transformed_expressions(f, assignee_f=None)[source]¶

Return a new copy of self where f has been applied to every expression occurring in self. args will be passed as extra arguments (in addition to the expression) to f.

If assignee_f is passed, then left-hand sides of assignments are passed to it. If it is not given, it defaults to the same as f.

write_dependency_names()[source]¶: Return a set of dependencies of the left hand side of the assignments performed by this instruction, including written variables and indices.

dependency_names()[source]¶

copy(**kwargs)[source]¶

Inherits from pytools.tag.Taggable.

Assignment objects¶

assignee¶

expression¶

The following attributes are only used until loopy.make_kernel() is finished:

temp_var_type¶: A loopy.Optional. If not empty, contains the type that will be assigned to the new temporary variable created from the assignment.

atomicity¶

A tuple of instances of VarAtomicity. Together, they describe to what extent the assignment is to be carried out in a way that involves atomic operations.

To describe an atomic update, any memory reads of exact occurrences of the left-hand side expression of the assignment in the right hand side are treated , together with the “memory write” part of the assignment, as part of one single atomic update.

Note

Exact identity of the LHS with RHS subexpressions is required for an atomic update to be recognized. For example, the following update will not be recognized as an update:

z[i] = z[i+1-1] + a {atomic}

loopy may choose to evaluate the right-hand side multiple times as part of a single assignment. It is up to the user to ensure that this retains correct semantics.

For example, the following assignment:

z[i] = f(z[i]) + a {atomic}

may generate the following (pseudo-)code:

DO
    READ ztemp_old = z[i]
    EVALUATE ztemp_new = f(ztemp_old) + a
WHILE compare_and_swap(z[i], ztemp_new, ztemp_old) did not succeed

__init__(assignee: str | Expression, expression: str | Expression, id: str | None = None, happens_after: Mapping[str, HappensAfter] | frozenset[str] | str | None = None, depends_on_is_final: bool | None = None, groups: frozenset[str] | None = None, conflicts_with_groups: frozenset[str] | None = None, no_sync_with: frozenset[tuple[str, str]] | None = None, within_inames_is_final: bool | None = None, within_inames: frozenset[str] | None = None, priority: int | None = None, predicates: frozenset[str] | None = None, tags: frozenset[Tag] | None = None, temp_var_type: type[_not_provided] | LoopyOptional | LoopyType | None = <class 'loopy.kernel.instruction._not_provided'>, atomicity: tuple[VarAtomicity, ...] = (), *, depends_on: frozenset[str] | str | None = None) → None[source]¶

Textual Assignment Syntax¶

The general syntax of an instruction is a simple assignment:

LHS[i,j,k] = EXPRESSION

Several extensions of this syntax are defined, as discussed below. They may be combined freely.

You can also use an instruction to declare a new temporary variable. (See Temporary Variables.) See Specifying Types for what types are acceptable. If the LHS has a subscript, bounds on the indices are inferred (which must be constants at the time of kernel creation) and the declared temporary is created as an array. Instructions declaring temporaries have the following form:

<temp_var_type> LHS[i,j,k] = EXPRESSION

You can also create a temporary and ask loopy to determine its type automatically. This uses the following syntax:

<> LHS[i,j,k] = EXPRESSION

Lastly, each instruction may optionally have a number of attributes specified, using the following format:

LHS[i,j,k] = EXPRESSION {attr1,attr2=value1:value2}

These are usually key-value pairs. The following attributes are recognized:

id=value sets the instruction’s identifier to value. value must be unique within the kernel. This identifier is used to refer to the instruction after it has been created, such as from dep attributes (see below) or from context matches.
id_prefix=value also sets the instruction’s identifier, however uniqueness is ensured by loopy itself, by appending further components (often numbers) to the given id_prefix.
inames=i:j:k forces the instruction to reside within the loops over Inames i, j and k (and only those).

Note

The default for the inames that the instruction depends on is the inames used in the instruction itself plus the common subset of inames shared by writers of all variables read by the instruction.

You can add a plus sign (”+”) to the front of this option value to indicate that you would like the inames you specify here to be in addition to the ones found by the heuristic described above.
dup=i:j->j_new:k->k_new makes a copy of the inames i, j, and k, with all the same domain constraints as the original inames. A new name of the copy of i will be automatically chosen, whereas the new name of j will be j_new, and the new name of k will be k_new.

This is a shortcut for calling loopy.duplicate_inames() later (once the kernel is created).
dep=id1:id2 creates a dependency of this instruction on the instructions with identifiers id1 and id2. The meaning of this dependency is that the code generated for this instruction is required to appear textually after all of these dependees’ generated code.

Identifiers here are allowed to be wildcards as defined by the Python function fnmatch.fnmatchcase(). This is helpful in conjunction with id_prefix.

Note

Since specifying all possible dependencies is cumbersome and error-prone, loopy employs a heuristic to automatically find dependencies. Specifically, loopy will automatically add a dependency to an instruction reading a variable if there is exactly one instruction writing that variable. (“Variable” here may mean either temporary variable or kernel argument.)

If each variable in a kernel is only written once, then this heuristic should be able to compute all required dependencies.

Conversely, if a variable is written by two different instructions, all ordering around that variable needs to be specified explicitly. It is recommended to use get_dot_dependency_graph() to visualize the dependency graph of possible orderings.

You may use a leading asterisk (”*”) to turn off the single-writer heuristic and indicate that the specified list of dependencies is exhaustive.
dep_query=... provides an alternative way of specifying instruction dependencies. The given string is parsed as a match expression object by loopy.match.parse_match(). Upon kernel generation, this match expression is used to match instructions in the kernel and add them as dependencies.
nosync=id1:id2 prescribes that no barrier synchronization is necessary for the instructions with identifiers id1 and id2, even if a dependency chain exists and variables are accessed in an apparently racy way.

Identifiers here are allowed to be wildcards as defined by the Python function fnmatch.fnmatchcase(). This is helpful in conjunction with id_prefix.

Identifiers (including wildcards) accept an optional @scope suffix, which prescribes that no synchronization at level scope is needed. This does not preclude barriers at levels different from scope. Allowable scope values are:
- local
- global
- any
As an example, nosync=id1@local:id2@global prescribes that no local synchronization is needed with instruction id1 and no global synchronization is needed with instruction id2.

nosync=id1@any has the same effect as nosync=id1.
nosync_query=... provides an alternative way of specifying nosync, just like dep_query and dep. As with nosync, nosync_query accepts an optional @scope suffix.
priority=integer sets the instructions priority to the value integer. Instructions with higher priority will be scheduled sooner, if possible. Note that the scheduler may still schedule a lower-priority instruction ahead of a higher-priority one if loop orders or dependencies require it.
if=variable1:variable2 Only execute this instruction if all condition variables (which must be scalar variables) evaluate to true (as defined by C).
tags=tag1:tag2 Apply tags to this instruction that can then be used for Matching contexts.
groups=group1:group2 Make this instruction part of the given instruction groups. See InstructionBase.groups.
conflicts_grp=group1:group2 Make this instruction conflict with the given instruction groups. See InstructionBase.conflicts_with_groups.
atomic The update embodied by the assignment is carried out atomically. See Assignment.atomicity for precise semantics.

Expressions¶

Loopy’s expressions are a slight superset of the expressions supported by pymbolic.

if(cond, then, else_)
a[[ 8*i + j ]]: Linear subscripts. See loopy.symbolic.LinearSubscript.
reductions See loopy.symbolic.Reduction.
- reduce vs simul_reduce
complex-valued arithmetic
tagging of array access and substitution rule use (“$”) See loopy.symbolic.TaggedVariable.
indexof, indexof_vec
cast(type, value): No parse syntax currently. See loopy.symbolic.TypeCast.
If constants in expressions are subclasses of numpy.generic, generated code will contain literals of exactly that type, making them explicitly typed. Constants given as Python types such as int, float or complex are called implicitly typed and adapt to the type of the expected result.

TODO: Functions TODO: Reductions

Function Call Instructions¶

class loopy.CallInstruction(assignees, expression, id=None, happens_after=None, depends_on_is_final=None, groups=None, conflicts_with_groups=None, no_sync_with=None, within_inames_is_final=None, within_inames=None, tags=None, temp_var_types=None, priority=0, predicates=frozenset({}), depends_on=None)[source]¶

An instruction capturing a function call. Unlike Assignment, this instruction supports functions with multiple return values.

assignees¶: A tuple of left-hand sides for the assignment

expression¶

The following attributes are only used until loopy.make_kernel() is finished:

temp_var_types¶: A tuple of :class:loopy.Optional. If an entry is not empty, it contains the type that will be assigned to the new temporary variable created from the assignment.

__init__(assignees, expression, id=None, happens_after=None, depends_on_is_final=None, groups=None, conflicts_with_groups=None, no_sync_with=None, within_inames_is_final=None, within_inames=None, tags=None, temp_var_types=None, priority=0, predicates=frozenset({}), depends_on=None)[source]¶

C Block Instructions¶

class loopy.CInstruction(iname_exprs, code, read_variables=frozenset({}), assignees=(), id=None, happens_after=None, depends_on_is_final=None, groups=None, conflicts_with_groups=None, no_sync_with=None, within_inames_is_final=None, within_inames=None, priority=0, predicates=frozenset({}), tags=None, depends_on=None)[source]¶

iname_exprs¶: A tuple of tuples (name, expr) of inames or expressions based on them that the instruction needs access to.

code¶

The C code to be executed.

The code should obey the following rules:

It should only write to temporary variables, specifically the temporary variables

Note

Of course, nothing in loopy will prevent you from doing ‘forbidden’ things in your C code. If you ignore the rules and something breaks, you get to keep both pieces.

read_variables¶: A frozenset of variable names that code reads. This is optional and only used for figuring out dependencies.

assignees¶: A sequence (typically a tuple) of variable references (with or without subscript) as pymbolic.typing.Expression instances that code writes to. This is optional and only used for figuring out dependencies.

Atomic Operations¶

class loopy.MemoryOrdering[source]¶

Ordering of atomic operations, defined as in C11 and OpenCL.

RELAXED¶

ACQUIRE¶

RELEASE¶

ACQ_REL¶

SEQ_CST¶

class loopy.MemoryScope[source]¶

Scope of atomicity, defined as in OpenCL.

auto¶: Scope matches the accessibility of the variable.

WORK_ITEM¶

WORK_GROUP¶

WORK_DEVICE¶

ALL_SVM_DEVICES¶

class loopy.VarAtomicity(var_name)[source]¶

A base class for the description of how atomic access to var_name shall proceed.

var_name¶

class loopy.OrderedAtomic(var_name)[source]¶

Properties of an atomic operation. A subclass of VarAtomicity.

ordering¶: One of the values from MemoryOrdering

scope¶: One of the values from MemoryScope

class loopy.AtomicInit(var_name)[source]¶

Describes initialization of an atomic variable. A subclass of OrderedAtomic.

ordering¶: One of the values from MemoryOrdering

scope¶: One of the values from MemoryScope

class loopy.AtomicUpdate(var_name)[source]¶

Properties of an atomic update. A subclass of OrderedAtomic.

ordering¶: One of the values from MemoryOrdering

scope¶: One of the values from MemoryScope

No-Op Instruction¶

class loopy.NoOpInstruction(id=None, happens_after=None, depends_on_is_final=None, groups=None, conflicts_with_groups=None, no_sync_with=None, within_inames_is_final=None, within_inames=None, priority=None, predicates=None, tags=None, depends_on=None)[source]¶

An instruction that carries out no operation. It is mainly useful as a way to structure dependencies between other instructions.

The textual syntax in a loopy kernel is:

... nop

Barrier Instructions¶

class loopy.BarrierInstruction(id, happens_after=None, depends_on_is_final=None, groups=None, conflicts_with_groups=None, no_sync_with=None, within_inames_is_final=None, within_inames=None, priority=None, predicates=None, tags=None, synchronization_kind='global', mem_kind='local', depends_on=None)[source]¶

An instruction that requires synchronization with all concurrent work items of synchronization_kind.

synchronization_kind¶: A string, "global" or "local".

mem_kind¶: A string, "global" or "local". Chooses which memory type to synchronize, for targets that require this (e.g. OpenCL)

The textual syntax in a loopy kernel is:

... gbarrier
... lbarrier

Note that the memory type mem_kind can be specified for local barriers:

... lbarrier {mem_kind=global}

Instruction Tags¶

class loopy.LegacyStringInstructionTag(value: str)[source]¶

A subclass of pytools.tag.Tag for use in InstructionBase.tags used for forward compatibility of the old string-based tagging mechanism. String-based tags are automatically converted to this type.

value¶

class loopy.UseStreamingStoreTag[source]¶: A subclass of pytools.tag.Tag for use in InstructionBase.tags used to indicate that if the instruction is an Assignment or a CallInstruction, then the ‘store’ part of the assignment should be realized using streaming stores.

Note

This tag is advisory in nature and may be ignored by targets that do not understand it or in situations where it does not apply.

Warning

This is a dodgy shortcut, and no promise is made that this will continue to work. Whether this is safe is target-dependent and program-dependent. No promise of safety is made.

Data: Arguments and Temporaries¶

Kernels operate on two types of data: ‘arguments’ carrying data into and out of a kernel, and temporaries with lifetimes tied to the runtime of the kernel.

Arguments¶

class loopy.KernelArgument(**kwargs)[source]¶

Base class for all argument types.

name¶

dtype¶

is_output¶

is_input¶

supporting_names() → frozenset[str][source]¶: ‘Supporting’ names are those that are likely to be required to be present for any use of the argument.

class loopy.ValueArg(name: str, dtype: ToLoopyTypeConvertible | None = None, approximately: int = 1000, is_output: bool = False, is_input: bool = True, tags: frozenset[Tag] | None = None)[source]¶

class loopy.ArrayArg(*args, **kwargs)[source]¶

name: str¶

dtype: LoopyType | None¶

The loopy.types.LoopyType of the array. If this is None, loopy will try to continue without knowing the type of this array, where the idea is that precise knowledge of the type will become available at invocation time. Calling the kernel (via loopy.LoopKernel.__call__()) automatically adds this type information based on invocation arguments.

Note that some transformations, such as loopy.add_padding() cannot be performed without knowledge of the exact dtype.

shape: ShapeType | type[auto] | None¶

May be one of the following:

None. In this case, no shape is intended to be specified, only the strides will be used to access the array. Bounds checking will not be performed.
loopy.auto. The shape will be determined by finding the access footprint.
a tuple like like numpy.ndarray.shape.

Each entry of the tuple is also allowed to be a pymbolic expression involving kernel parameters, or a (potentially-comma separated) or a string that can be parsed to such an expression.

Any element of the shape tuple not used to compute strides may be None.

dim_tags: Sequence[ArrayDimImplementationTag] | None¶: See Data Axis Tags.

offset: Expression | str | None¶

Offset from the beginning of the buffer to the point from which the strides are counted, in units of the dtype. May be one of

0 or None
a string (that is interpreted as an argument name).
a pymbolic expression
loopy.auto, in which case an offset argument is added automatically, immediately following this argument.

dim_names: tuple[str, ...] | None¶: A tuple of strings providing names for the array axes, or None. If given, must have the same number of entries as dim_tags and dim_tags. These do not live in any particular namespace (i.e. collide with no other names) and serve a purely informational/documentational purpose. On occasion, they are used to generate more informative names than could be achieved by axis numbers.

alignment: int | None¶

Memory alignment of the array in bytes. For temporary arrays, this ensures they are allocated with this alignment. For arguments, this entails a promise that the incoming array obeys this alignment restriction.

Defaults to None.

If an integer N is given, the array would be declared with __attribute__((aligned(N))) in code generation for loopy.CFamilyTarget.

Added in version 2018.1.

tags: frozenset[Tag]¶: A (possibly empty) frozenset of instances of pytools.tag.Tag intended for consumption by an application.

Added in version 2020.2.2.

__init__(*args, **kwargs)[source]¶

All of the following (except name) are optional. Specify either strides or shape.

Parameters:

name – When passed to loopy.make_kernel, this may contain multiple names separated by commas, in which case multiple arguments, each with identical properties, are created for each name.
shape – May be any of the things specified under shape, or a string which can be parsed into the previous form.
dim_tags – A comma-separated list of tags as understood by loopy.kernel.array.parse_array_dim_tags().
strides –
May be one of the following:
- None
- loopy.auto. The strides will be determined by order and the access footprint.
- a tuple like like numpy.ndarray.shape.
  
  Each entry of the tuple is also allowed to be a pymbolic expression involving kernel parameters, or a (potentially-comma separated) or a string that can be parsed to such an expression.
- A string which can be parsed into the previous form.
order – “F” or “C” for C (row major) or Fortran (column major). Defaults to the default_order argument passed to loopy.make_kernel().
for_atomic – Whether the array is declared for atomic access, and, if necessary, using atomic-capable data types.
offset – (See offset)
alignment – memory alignment in bytes
tags – An instance of or an Iterable of instances of pytools.tag.Tag.

__eq__(other)[source]¶: Return self==value.

num_user_axes(require_answer=True)[source]¶

num_target_axes()[source]¶

vector_size(target: TargetBase) → int[source]¶

Return the size of the vector type used for the array divided by the basic data type.

Note: For 3-vectors, this will be 4.

(supports persistent hashing)

address_space¶

An attribute of AddressSpace defining the address space in which the array resides.

is_output¶

An instance of bool. If set to True, the array is used to return information to the caller. If set to False, the callee does not write to the array during a call.

is_input¶

An instance of bool. If set to True, expected to be provided by the caller. If False, the callee does not depend on the array at kernel entry.

class loopy.ConstantArg(*args, **kwargs)[source]¶

name: str¶

dtype: LoopyType | None¶

Note that some transformations, such as loopy.add_padding() cannot be performed without knowledge of the exact dtype.

shape: ShapeType | type[auto] | None¶

May be one of the following:

None. In this case, no shape is intended to be specified, only the strides will be used to access the array. Bounds checking will not be performed.
loopy.auto. The shape will be determined by finding the access footprint.
a tuple like like numpy.ndarray.shape.

Each entry of the tuple is also allowed to be a pymbolic expression involving kernel parameters, or a (potentially-comma separated) or a string that can be parsed to such an expression.

Any element of the shape tuple not used to compute strides may be None.

dim_tags: Sequence[ArrayDimImplementationTag] | None¶: See Data Axis Tags.

offset: Expression | str | None¶

Offset from the beginning of the buffer to the point from which the strides are counted, in units of the dtype. May be one of

0 or None
a string (that is interpreted as an argument name).
a pymbolic expression
loopy.auto, in which case an offset argument is added automatically, immediately following this argument.

dim_names: tuple[str, ...] | None¶: A tuple of strings providing names for the array axes, or None. If given, must have the same number of entries as dim_tags and dim_tags. These do not live in any particular namespace (i.e. collide with no other names) and serve a purely informational/documentational purpose. On occasion, they are used to generate more informative names than could be achieved by axis numbers.

alignment: int | None¶

Defaults to None.

If an integer N is given, the array would be declared with __attribute__((aligned(N))) in code generation for loopy.CFamilyTarget.

Added in version 2018.1.

tags: frozenset[Tag]¶: A (possibly empty) frozenset of instances of pytools.tag.Tag intended for consumption by an application.

Added in version 2020.2.2.

__init__(*args, **kwargs)[source]¶

All of the following (except name) are optional. Specify either strides or shape.

Parameters:

name – When passed to loopy.make_kernel, this may contain multiple names separated by commas, in which case multiple arguments, each with identical properties, are created for each name.
shape – May be any of the things specified under shape, or a string which can be parsed into the previous form.
dim_tags – A comma-separated list of tags as understood by loopy.kernel.array.parse_array_dim_tags().
strides –
May be one of the following:
- None
- loopy.auto. The strides will be determined by order and the access footprint.
- a tuple like like numpy.ndarray.shape.
  
  Each entry of the tuple is also allowed to be a pymbolic expression involving kernel parameters, or a (potentially-comma separated) or a string that can be parsed to such an expression.
- A string which can be parsed into the previous form.
order – “F” or “C” for C (row major) or Fortran (column major). Defaults to the default_order argument passed to loopy.make_kernel().
for_atomic – Whether the array is declared for atomic access, and, if necessary, using atomic-capable data types.
offset – (See offset)
alignment – memory alignment in bytes
tags – An instance of or an Iterable of instances of pytools.tag.Tag.

__eq__(other)[source]¶: Return self==value.

num_user_axes(require_answer=True)[source]¶

num_target_axes()[source]¶

vector_size(target: TargetBase) → int[source]¶

Return the size of the vector type used for the array divided by the basic data type.

Note: For 3-vectors, this will be 4.

(supports persistent hashing)

class loopy.ImageArg(*args, **kwargs)[source]¶

name: str¶

dtype: LoopyType | None¶

Note that some transformations, such as loopy.add_padding() cannot be performed without knowledge of the exact dtype.

shape: ShapeType | type[auto] | None¶

May be one of the following:

None. In this case, no shape is intended to be specified, only the strides will be used to access the array. Bounds checking will not be performed.
loopy.auto. The shape will be determined by finding the access footprint.
a tuple like like numpy.ndarray.shape.

Each entry of the tuple is also allowed to be a pymbolic expression involving kernel parameters, or a (potentially-comma separated) or a string that can be parsed to such an expression.

Any element of the shape tuple not used to compute strides may be None.

dim_tags: Sequence[ArrayDimImplementationTag] | None¶: See Data Axis Tags.

offset: Expression | str | None¶

Offset from the beginning of the buffer to the point from which the strides are counted, in units of the dtype. May be one of

0 or None
a string (that is interpreted as an argument name).
a pymbolic expression
loopy.auto, in which case an offset argument is added automatically, immediately following this argument.

dim_names: tuple[str, ...] | None¶: A tuple of strings providing names for the array axes, or None. If given, must have the same number of entries as dim_tags and dim_tags. These do not live in any particular namespace (i.e. collide with no other names) and serve a purely informational/documentational purpose. On occasion, they are used to generate more informative names than could be achieved by axis numbers.

alignment: int | None¶

Defaults to None.

If an integer N is given, the array would be declared with __attribute__((aligned(N))) in code generation for loopy.CFamilyTarget.

Added in version 2018.1.

tags: frozenset[Tag]¶: A (possibly empty) frozenset of instances of pytools.tag.Tag intended for consumption by an application.

Added in version 2020.2.2.

__init__(*args, **kwargs)[source]¶

All of the following (except name) are optional. Specify either strides or shape.

Parameters:

name – When passed to loopy.make_kernel, this may contain multiple names separated by commas, in which case multiple arguments, each with identical properties, are created for each name.
shape – May be any of the things specified under shape, or a string which can be parsed into the previous form.
dim_tags – A comma-separated list of tags as understood by loopy.kernel.array.parse_array_dim_tags().
strides –
May be one of the following:
- None
- loopy.auto. The strides will be determined by order and the access footprint.
- a tuple like like numpy.ndarray.shape.
  
  Each entry of the tuple is also allowed to be a pymbolic expression involving kernel parameters, or a (potentially-comma separated) or a string that can be parsed to such an expression.
- A string which can be parsed into the previous form.
order – “F” or “C” for C (row major) or Fortran (column major). Defaults to the default_order argument passed to loopy.make_kernel().
for_atomic – Whether the array is declared for atomic access, and, if necessary, using atomic-capable data types.
offset – (See offset)
alignment – memory alignment in bytes
tags – An instance of or an Iterable of instances of pytools.tag.Tag.

__eq__(other)[source]¶: Return self==value.

num_user_axes(require_answer=True)[source]¶

num_target_axes()[source]¶

vector_size(target: TargetBase) → int[source]¶

Return the size of the vector type used for the array divided by the basic data type.

Note: For 3-vectors, this will be 4.

(supports persistent hashing)

Temporary Variables¶

Temporary variables model OpenCL’s private and local address spaces. Both have the lifetime of a kernel invocation.

class loopy.AddressSpace(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶

Storage location of a variable.

PRIVATE¶

LOCAL¶

GLOBAL¶

class loopy.TemporaryVariable(name: str, dtype: ToLoopyTypeConvertible = None, shape: ShapeType | type[auto] | None = <class 'loopy.typing.auto'>, address_space: AddressSpace | type[auto] | None = None, dim_tags: Sequence[ArrayDimImplementationTag] | None = None, offset: Expression | str | None = 0, dim_names: tuple[str, ...] | None = None, strides: tuple[Expression, ...] | None = None, order: str | None = None, base_indices: tuple[Expression, ...] | None = None, storage_shape: ShapeType | None = None, base_storage: str | None = None, initializer: np.ndarray | None = None, read_only: bool = False, _base_storage_access_may_be_aliasing: bool = False, **kwargs: Any)[source]¶

name: str¶

dtype: LoopyType | None¶

Note that some transformations, such as loopy.add_padding() cannot be performed without knowledge of the exact dtype.

shape: ShapeType | type[auto] | None¶

May be one of the following:

None. In this case, no shape is intended to be specified, only the strides will be used to access the array. Bounds checking will not be performed.
loopy.auto. The shape will be determined by finding the access footprint.
a tuple like like numpy.ndarray.shape.

Each entry of the tuple is also allowed to be a pymbolic expression involving kernel parameters, or a (potentially-comma separated) or a string that can be parsed to such an expression.

Any element of the shape tuple not used to compute strides may be None.

dim_tags: Sequence[ArrayDimImplementationTag] | None¶: See Data Axis Tags.

offset: Expression | str | None¶

Offset from the beginning of the buffer to the point from which the strides are counted, in units of the dtype. May be one of

0 or None
a string (that is interpreted as an argument name).
a pymbolic expression
loopy.auto, in which case an offset argument is added automatically, immediately following this argument.

dim_names: tuple[str, ...] | None¶: A tuple of strings providing names for the array axes, or None. If given, must have the same number of entries as dim_tags and dim_tags. These do not live in any particular namespace (i.e. collide with no other names) and serve a purely informational/documentational purpose. On occasion, they are used to generate more informative names than could be achieved by axis numbers.

alignment: int | None¶

Defaults to None.

If an integer N is given, the array would be declared with __attribute__((aligned(N))) in code generation for loopy.CFamilyTarget.

Added in version 2018.1.

tags: frozenset[Tag]¶: A (possibly empty) frozenset of instances of pytools.tag.Tag intended for consumption by an application.

Added in version 2020.2.2.

__init__(name: str, dtype: ToLoopyTypeConvertible = None, shape: ShapeType | type[auto] | None = <class 'loopy.typing.auto'>, address_space: AddressSpace | type[auto] | None = None, dim_tags: Sequence[ArrayDimImplementationTag] | None = None, offset: Expression | str | None = 0, dim_names: tuple[str, ...] | None = None, strides: tuple[Expression, ...] | None = None, order: str | None = None, base_indices: tuple[Expression, ...] | None = None, storage_shape: ShapeType | None = None, base_storage: str | None = None, initializer: np.ndarray | None = None, read_only: bool = False, _base_storage_access_may_be_aliasing: bool = False, **kwargs: Any) → None[source]¶

Parameters:

dtype – loopy.auto or a numpy.dtype
shape – loopy.auto or a shape tuple
base_indices – loopy.auto or a tuple of base indices

__eq__(other)[source]¶: Return self==value.

num_user_axes(require_answer=True)[source]¶

num_target_axes()[source]¶

vector_size(target: TargetBase) → int[source]¶

Return the size of the vector type used for the array divided by the basic data type.

Note: For 3-vectors, this will be 4.

(supports persistent hashing)

storage_shape: ShapeType | None¶

base_indices: tuple[Expression, ...] | None¶

address_space: AddressSpace | type[auto]¶

base_storage: str | None¶: The name of a storage array that is to be used to actually hold the data in this temporary, or None. If not None or the name of an existing variable, a variable of this name and appropriate size will be created.

initializer: numpy.ndarray | None¶: None or a numpy.ndarray of data to be used to initialize the array.

read_only: bool¶: A bool indicating whether the variable may be written during its lifetime. If True, initializer must be given.

_base_storage_access_may_be_aliasing: bool¶: Whether the temporary is used to alias the underlying base storage. Defaults to False. If False, C-based code generators will declare the temporary as a restrict const pointer to the base storage memory location. If True, the restrict part is omitted on this declaration.

Specifying Types¶

loopy uses the same type system as numpy. (See numpy.dtype) It also uses pyopencl for a registry of user-defined types and their C equivalents. See pyopencl.tools.get_or_register_dtype() and related functions.

For a string representation of types, all numpy types (e.g. float32 etc.) are accepted, in addition to what is registered in pyopencl.

Data Axis Tags¶

Data axis tags specify how a multi-dimensional array (which is loopy’s main way of storing data) is represented in (linear, 1D) computer memory. This storage format is given as a number of “tags”, as listed in the table below. Each axis of an array has a tag corresponding to it. In the user interface, array dim tags are specified as a tuple of these tags or a comma-separated string containing them, such as the following:

c,vec,sep,c

The interpretation of these tags is order-dependent, they are read from left to right.

Tag	Meaning
`c`	Nest current axis around the ones that follow
`f`	Nest current axis inside the ones that follow
`N0` … `N9`	Specify an explicit nesting level for this axis
`stride:EXPR`	A fixed stride
`sep`	Implement this axis by mapping to separate arrays
`vec`	Implement this axis as entries in a vector

sep and vec obviously require the number of entries in the array along their respective axis to be known at code generation time.

When the above speaks about ‘nesting levels’, this means that axes “nested inside” others are “faster-moving” when viewed from linear memory.

In addition, each tag may be followed by a question mark (?), which indicates that if there are more dimension tags specified than array axes present, that this axis should be omitted. Axes with question marks are omitted in a left-first manner until the correct number of dimension tags is achieved.

Some examples follow, all of which use a three-dimensional array of shape (3, M, 4). For simplicity, we assume that array entries have size one.

c,c,c: The axes will have strides (M*4, 4, 1), leading to a C-like / row-major layout.
f,f,f: The axes will have strides (1, 3, 3*M), leading to a Fortran-like / row-major layout.
sep,c,c: The array will be mapped to three arrays of shape (M, 4), each with strides (4, 1).
c,c,vec: The array will be mapped to an array of float4 vectors, with (float4-based) strides of (M, 1).
N1,N0,N2: The axes will have strides (M, 1, 3*M).

Substitution Rules¶

Substitution Rule Objects¶

class loopy.SubstitutionRule(name: str, arguments: Sequence[str], expression: Expression)[source]¶

name: str¶

arguments: Sequence[str]¶

expression: Expression¶

Textual Syntax for Substitution Rules¶

Syntax of a substitution rule:

rule_name(arg1, arg2) := EXPRESSION

Kernel Options¶

class loopy.Options(**kwargs: Any)[source]¶

Unless otherwise specified, these options are Boolean-valued (i.e. on/off).

Code-generation options

annotate_inames¶: When generating code for inames, annotate them with comments if it is not immediately apparent which iname is being referred to (such as for inames mapped to constants or OpenCL group/local IDs).

trace_assignments¶: Generate code that uses printf in kernels to trace the execution of assignment instructions.

trace_assignment_values¶: Like trace_assignments, but also trace the assigned values.

check_dep_resolution¶: Whether loopy should issue an error if a dependency expression does not match any instructions in the kernel.

Invocation-related options

skip_arg_checks¶: Do not do any checking (data type, data layout, shape, etc.) on arguments for a minor performance gain.

Changed in version 2021.1: This now defaults to the same value as the optimize sub-flag from sys.flags. This flag can be controlled (i.e. set to True) by running Python with the -O flag.

no_numpy¶

Do not check for or accept numpy arrays as arguments.

Defaults to False.

cl_exec_manage_array_events¶

Within the PyOpenCL executor, respect and update pyopencl.array.Array.events.

Defaults to True.

return_dict¶

Have kernels return a dict instead of a tuple as output. Specifically, the result of a kernel invocation with this flag is a tuple (evt, out_dict), where out_dict is a dictionary mapping argument names to their output values. This is helpful if arguments are inferred and argument ordering is thus implementation-defined.

See ExecutorBase.__call__().

write_wrapper¶: Print the generated Python invocation wrapper. Accepts a file name as a value. Writes to sys.stdout if none is given.

write_code¶: Print the generated code. Accepts a file name or a boolean as a value. Writes to sys.stdout if set to True.

edit_code¶: Invoke an editor (given by the environment variable EDITOR) on the generated kernel code, allowing for tweaks before the code is passed on to the target for compilation.

allow_fp_reordering¶: Allow re-ordering of floating point arithmetic. Re-ordering may give different results as floating point arithmetic is not associative in addition and multiplication. Default is True. Note that the implementation of this option is currently incomplete.

build_options¶: Options to pass to the target compiler when building the kernel. A list of strings.

allow_terminal_colors¶: A bool. Whether to allow colors in terminal output

Features

disable_global_barriers¶

enforce_variable_access_ordered¶

If True, require that loopy.check.check_variable_access_ordered() passes. Required for language versions 2018.1 and above. This check helps find and eliminate unintentionally unordered access to variables.

If equal to "no_check", then no check is performed.

enforce_array_accesses_within_bounds¶

If True, require that check_bounds() passes. If False, then check_bounds() raises a warning for any out-of-bounds accesses.

If equal to "no_check", then no check is performed.

insert_gbarriers¶: If True, based on the memory dependency between variables in the global address space loopy will insert global barriers to avoid RAW, WAR and WAW races.

Targets¶

class loopy.TargetBase[source]¶

Base class for all targets, i.e. different combinations of code that loopy can generate.

Objects of this type must be picklable.

class loopy.ASTBuilderBase(target)[source]¶: An interface for generating (host or device) ASTs.

class loopy.CFamilyTarget(fortran_abi=False)[source]¶: A target for “least-common denominator C”, without any parallel extensions, and without use of any C99 specifics. Intended to be usable as a common base for C99, C++, OpenCL, CUDA, and the like.

class loopy.CTarget(fortran_abi=False)[source]¶: This target may emit code using all features of C99. For a target base supporting “least-common-denominator” C, see CFamilyTarget.

class loopy.ExecutableCTarget(compiler=None, fortran_abi=False)[source]¶: An executable CFamilyTarget that uses (by default) JIT compilation of C-code

class loopy.CudaTarget(extern_c=True)[source]¶: A target for Nvidia’s CUDA GPU programming language.

class loopy.OpenCLTarget(atomics_flavor=None, use_int8_for_bool=True)[source]¶: A target for the OpenCL C heterogeneous compute programming language.

class loopy.PyOpenCLTarget(device=None, *, pyopencl_module_name: str = '_lpy_cl', atomics_flavor=None, use_int8_for_bool: bool = True, limit_arg_size_nbytes: int | None = None, pointer_size_nbytes: int | None = None)[source]¶: A code generation target that takes special advantage of pyopencl features such as run-time knowledge of the target device (to generate warnings) and support for complex numbers.

class loopy.ISPCTarget(fortran_abi=False)[source]¶: A code generation target for Intel’s ISPC SPMD programming language, to target Intel’s Knight’s hardware and modern Intel CPUs with wide vector units.

Helper values¶

class loopy.UniqueName(name)[source]¶: A tag for a string that identifies a partial identifier that is to be made unique by the UI.

class loopy.Optional(value=<class 'loopy.tools._no_value'>)[source]¶

A wrapper for an optionally present object.

has_value¶: True if and only if this object contains a value.

value¶: The value, if present.

Libraries: Extending and Interfacing with External Functionality¶

Symbols¶

Functions¶

class loopy.PreambleInfo(kernel: LoopKernel, seen_dtypes: set[LoopyType], seen_functions: set[SeenFunction], seen_atomic_dtypes: set[LoopyType], codegen_state: CodeGenerationState)[source]¶

kernel: LoopKernel¶

seen_dtypes: set[LoopyType]¶

seen_functions: set[SeenFunction]¶

seen_atomic_dtypes: set[LoopyType]¶

class loopy.CallMangleInfo(target_name, result_dtypes, arg_dtypes)[source]¶

target_name¶: A string. The name of the function to be called in the generated target code.

result_dtypes¶: A tuple of loopy.types.LoopyType instances indicating what types of values the function returns.

arg_dtypes¶: A tuple of loopy.types.LoopyType instances indicating what types of arguments the function actually receives.

Reductions¶

The Kernel Object¶

Do not create LoopKernel objects directly. Instead, refer to Reference: Creating Kernels.

class loopy.LoopKernel(domains: Sequence[islpy.BasicSet], instructions: Sequence[InstructionBase], args: Sequence[KernelArgument], assumptions: islpy.BasicSet, temporary_variables: Mapping[str, TemporaryVariable], inames: Mapping[InameStr, loopy.kernel.data.Iname], substitutions: Mapping[str, SubstitutionRule], options: Options, target: TargetBase, tags: frozenset[Tag], state: KernelState = KernelState.INITIAL, name: str = 'loopy_kernel', preambles: Sequence[tuple[int, str]] = (), preamble_generators: Sequence[Callable[[loopy.codegen.PreambleInfo], Iterator[tuple[int, str]]]] = (), symbol_manglers: Sequence[Callable[[LoopKernel, str], tuple[LoopyType, str] | None]] = (), linearization: Sequence[ScheduleItem] | None = None, iname_slab_increments: Mapping[InameStr, tuple[int, int]] = <factory>, loop_priority: frozenset[tuple[InameStr, ...]] = <factory>, applied_iname_rewrites: tuple[dict[InameStr, Expression], ...] = (), index_dtype: NumpyType = np:dtype('int32'), silenced_warnings: frozenset[str] = frozenset({}), overridden_get_grid_sizes_for_insn_ids: Callable[[frozenset[str], dict[str, InKernelCallable], bool], tuple[tuple[int, ...], tuple[int, ...]]] | None = None)[source]¶

These correspond more or less directly to arguments of loopy.make_kernel().

Note

This data structure and its attributes should be considered immutable, even if it contains mutable data types. See copy() for an easy way of producing a modified copy.

domains: Sequence[islpy.BasicSet]¶: Represents the Loop Domain Forest.

instructions: Sequence[InstructionBase]¶: See Instructions.

args: Sequence[KernelArgument]¶

linearization: Sequence[ScheduleItem] | None = None¶

name: str = 'loopy_kernel'¶

preambles: Sequence[tuple[int, str]] = ()¶

preamble_generators: Sequence[Callable[[loopy.codegen.PreambleInfo], Iterator[tuple[int, str]]]] = ()¶

assumptions: islpy.BasicSet¶: Must be a islpy.BasicSet parameter domain.

temporary_variables: Mapping[str, TemporaryVariable]¶

symbol_manglers: Sequence[Callable[[LoopKernel, str], tuple[LoopyType, str] | None]] = ()¶

substitutions: Mapping[str, SubstitutionRule]¶

iname_slab_increments: Mapping[InameStr, tuple[int, int]]¶: A mapping from inames to (lower_incr, upper_incr) tuples that will be separated out in the execution to generate ‘bulk’ slabs with fewer conditionals.

loop_priority: frozenset[tuple[InameStr, ...]]¶: A frozenset of priority constraints to the kernel. Each such constraint is a tuple of inames. Inames occurring in such a tuple will be scheduled earlier than any iname following in the tuple. This applies only to inames with non-parallel implementation tags.

silenced_warnings: frozenset[str] = frozenset({})¶

applied_iname_rewrites: tuple[dict[InameStr, Expression], ...] = ()¶: A list of past substitution dictionaries that were applied to the kernel. These are stored so that they may be repeated on expressions the user specifies later.

options: Options¶

state: KernelState = 0¶

target: TargetBase¶

inames: Mapping[InameStr, loopy.kernel.data.Iname]¶: An entry is guaranteed to be present for each iname.

__call__(*args, **kwargs)[source]¶: Execute the LoopKernel.

copy(**kwargs: Any) → LoopKernel[source]¶

tagged(tags: Iterable[Tag] | Tag | None) → Self[source]¶

Return a copy of self with the specified tag or tags added to the set of tags. If the resulting set of tags violates the rules on pytools.tag.UniqueTag, an error is raised.

Parameters:: tags – An instance of Tag or an iterable with instances therein.

without_tags(tags: Iterable[Tag] | Tag | None, verify_existence: bool = True) → Self[source]¶

Return a copy of self without the specified tags.

Parameters:

tags – An instance of Tag or an iterable with instances therein.
verify_existence – If set to True, this method raises an exception if not all tags specified for removal are present in the original set of tags. Default True.

class loopy.KernelState(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶

CALLS_RESOLVED = 1¶

INITIAL = 0¶

LINEARIZED = 3¶

PREPROCESSED = 2¶

Implementation Details: The Base Array¶

All array-like data in loopy (such as ArrayArg and TemporaryVariable) derive from single, shared base array type, described next.

class loopy.kernel.array.ArrayBase(name, dtype=None, shape=None, dim_tags=None, offset=0, dim_names=None, strides=None, order=None, for_atomic=False, alignment=None, tags=None, **kwargs)[source]¶

name: str¶

dtype: LoopyType | None¶

Note that some transformations, such as loopy.add_padding() cannot be performed without knowledge of the exact dtype.

shape: ShapeType | type[auto] | None¶

May be one of the following:

None. In this case, no shape is intended to be specified, only the strides will be used to access the array. Bounds checking will not be performed.
loopy.auto. The shape will be determined by finding the access footprint.
a tuple like like numpy.ndarray.shape.

Each entry of the tuple is also allowed to be a pymbolic expression involving kernel parameters, or a (potentially-comma separated) or a string that can be parsed to such an expression.

Any element of the shape tuple not used to compute strides may be None.

dim_tags: Sequence[ArrayDimImplementationTag] | None¶: See Data Axis Tags.

offset: Expression | str | None¶

Offset from the beginning of the buffer to the point from which the strides are counted, in units of the dtype. May be one of

0 or None
a string (that is interpreted as an argument name).
a pymbolic expression
loopy.auto, in which case an offset argument is added automatically, immediately following this argument.

dim_names: tuple[str, ...] | None¶: A tuple of strings providing names for the array axes, or None. If given, must have the same number of entries as dim_tags and dim_tags. These do not live in any particular namespace (i.e. collide with no other names) and serve a purely informational/documentational purpose. On occasion, they are used to generate more informative names than could be achieved by axis numbers.

alignment: int | None¶

Defaults to None.

If an integer N is given, the array would be declared with __attribute__((aligned(N))) in code generation for loopy.CFamilyTarget.

Added in version 2018.1.

tags: frozenset[Tag]¶: A (possibly empty) frozenset of instances of pytools.tag.Tag intended for consumption by an application.

Added in version 2020.2.2.

__init__(name, dtype=None, shape=None, dim_tags=None, offset=0, dim_names=None, strides=None, order=None, for_atomic=False, alignment=None, tags=None, **kwargs)[source]¶

All of the following (except name) are optional. Specify either strides or shape.

Parameters:

name – When passed to loopy.make_kernel, this may contain multiple names separated by commas, in which case multiple arguments, each with identical properties, are created for each name.
shape – May be any of the things specified under shape, or a string which can be parsed into the previous form.
dim_tags – A comma-separated list of tags as understood by loopy.kernel.array.parse_array_dim_tags().
strides –
May be one of the following:
- None
- loopy.auto. The strides will be determined by order and the access footprint.
- a tuple like like numpy.ndarray.shape.
  
  Each entry of the tuple is also allowed to be a pymbolic expression involving kernel parameters, or a (potentially-comma separated) or a string that can be parsed to such an expression.
- A string which can be parsed into the previous form.
order – “F” or “C” for C (row major) or Fortran (column major). Defaults to the default_order argument passed to loopy.make_kernel().
for_atomic – Whether the array is declared for atomic access, and, if necessary, using atomic-capable data types.
offset – (See offset)
alignment – memory alignment in bytes
tags – An instance of or an Iterable of instances of pytools.tag.Tag.

__eq__(other)[source]¶: Return self==value.

num_user_axes(require_answer=True)[source]¶

num_target_axes()[source]¶

vector_size(target: TargetBase) → int[source]¶

Return the size of the vector type used for the array divided by the basic data type.

Note: For 3-vectors, this will be 4.

(supports persistent hashing)