forked from OSchip/llvm-project
[mlir] Introduce more intuitive wording for attributes.
After discussion, it seems like we want to go with "inherent/discardable". These seem to best capture the relationship with the op semantics and don't conflict with other terms. Please let me know your preferences. Some of the other contenders are: ``` "intrinsic" side | "annotation" side -----------------+------------------ characteristic | annotation closed | open definitional | advisory essential | discardable expected | unexpected innate | acquired internal | external intrinsic | extrinsic known | unknown local | global native | foreign inherent | acquired ``` Rationale: - discardable: good. discourages use for stable data. - inherent: good - annotation: redundant and doesn't convey difference - intrinsic: confusable with "compiler intrinsics". - definitional: too much of a mounthful - extrinsic: too exotic of a word and hard to say - acquired: doesn't convey the relationship to the semantics - internal/external: not immediately obvious: what is internal to what? - innate: similar to intrinsic but worse - acquired: we don't typically think of an op as "acquiring" things - known/unknown: by who? - local/global: to what? - native/foreign: to where? - advisory: confusing distinction: is the attribute itself advisory or is the information it provides advisory? - essential: an intrinsic attribute need not be present. - expected: same issue as essential - unexpected: by who/what? - closed/open: whether the set is open or closed doesn't seem essential to the attribute being intrinsic. Also, in theory an op can have an unbounded set of intrinsic attributes (e.g. `arg<N>` for func). - characteristic: unless you have a math background this probably doesn't make as much sense Differential Revision: https://reviews.llvm.org/D96093
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@ -304,9 +304,9 @@ operations](#target-specific-operations).
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The internal representation of an operation is simple: an operation is
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identified by a unique string (e.g. `dim`, `tf.Conv2d`, `x86.repmovsb`,
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`ppc.eieio`, etc), can return zero or more results, take zero or more
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operands, may have zero or more attributes, may have zero or more successors,
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and zero or more enclosed [regions](#regions). The generic printing form
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includes all these elements literally, with a function type to indicate the
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operands, has a dictionary of [attributes](#attributes), has zero or more
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successors, and zero or more enclosed [regions](#regions). The generic printing
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form includes all these elements literally, with a function type to indicate the
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types of the results and operands.
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Example:
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@ -321,7 +321,7 @@ Example:
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// Invoke a TensorFlow function called tf.scramble with two inputs
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// and an attribute "fruit".
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%2 = "tf.scramble"(%result#0, %bar) {fruit: "banana"} : (f32, i32) -> f32
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%2 = "tf.scramble"(%result#0, %bar) {fruit = "banana"} : (f32, i32) -> f32
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```
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In addition to the basic syntax above, dialects may register known operations.
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@ -1308,34 +1308,37 @@ shape `(0, 42)` and zero shapes are not allowed.
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Syntax:
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```
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attribute-entry ::= dialect-attribute-entry | dependent-attribute-entry
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dialect-attribute-entry ::= dialect-namespace `.` bare-id `=` attribute-value
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dependent-attribute-entry ::= dependent-attribute-name `=` attribute-value
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dependent-attribute-name ::= ((letter|[_]) (letter|digit|[_$])*)
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| string-literal
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attribute-entry ::= (bare-id | string-literal) `=` attribute-value
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attribute-value ::= attribute-alias | dialect-attribute | builtin-attribute
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```
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Attributes are the mechanism for specifying constant data on operations in
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places where a variable is never allowed - e.g. the comparison predicate of a
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[`cmpi` operation](Dialects/Standard.md#stdcmpi-cmpiop), or the stride of a
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convolution. They consist of a name and a concrete attribute value. The set of
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expected attributes, their structure, and their interpretation are all
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contextually dependent on what they are attached to.
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[`cmpi` operation](Dialects/Standard.md#stdcmpi-cmpiop). Each operation has an
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attribute dictionary, which associates a set of attribute names to attribute
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values. MLIR's builtin dialect provides a rich set of
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[builtin attribute values](#builtin-attribute-values) out of the box (such as
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arrays, dictionaries, strings, etc.). Additionally, dialects can define their
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own [dialect attribute values](#dialect-attribute-values).
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There are two main classes of attributes: dependent and dialect. Dependent
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attributes derive their structure and meaning from what they are attached to;
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e.g., the meaning of the `index` attribute on a `dim` operation is defined by
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the `dim` operation. Dialect attributes, on the other hand, derive their context
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and meaning from a specific dialect. An example of a dialect attribute may be a
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`swift.self` function argument attribute that indicates an argument is the
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self/context parameter. The context of this attribute is defined by the `swift`
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dialect and not the function argument.
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The top-level attribute dictionary attached to an operation has special
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semantics. The attribute entries are considered to be of two different kinds
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based on whether their dictionary key has a dialect prefix:
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Attribute values are represented by the following forms:
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- *inherent attributes* are inherent to the definition of an operation's
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semantics. The operation itself is expected to verify the consistency of these
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attributes. An example is the `predicate` attribute of the `std.cmpi` op.
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These attributes must have names that do not start with a dialect prefix.
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```
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attribute-value ::= attribute-alias | dialect-attribute | builtin-attribute
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```
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- *discardable attributes* have semantics defined externally to the operation
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itself, but must be compatible with the operations's semantics. These
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attributes must have names that start with a dialect prefix. The dialect
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indicated by the dialect prefix is expected to verify these attributes. An
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example is the `gpu.container_module` attribute.
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Note that attribute values are allowed to themselves be dictionary attributes,
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but only the top-level dictionary attribute attached to the operation is subject
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to the classification above.
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### Attribute Value Aliases
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@ -1404,8 +1407,8 @@ attribute values.
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### Builtin Attribute Values
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Builtin attributes are a core set of
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[dialect attributes](#dialect-attribute-values) that are defined in a builtin
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dialect and thus available to all users of MLIR.
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[dialect attribute values](#dialect-attribute-values) that are defined in a
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builtin dialect and thus available to all users of MLIR.
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```
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builtin-attribute ::= affine-map-attribute
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