This required changing a bit of how attributes/types are parsed. A new
`KeywordSwitch` class was added to AsmParser that provides a StringSwitch
like API for parsing keywords with a set of potential matches. It intends to
both provide a cleaner API, and enable injection for code completion. This
required changing the API of `generated(Attr|Type)Parser` to handle the
parsing of the keyword, instead of having the user do it. Most upstream
dialects use the autogenerated handling and didn't require a direct update.
Differential Revision: https://reviews.llvm.org/D129267
This commit adds code completion results to the MLIR LSP using
a new code completion context in the MLIR parser. This commit
adds initial completion for dialect, operation, SSA value, and
block names.
Differential Revision: https://reviews.llvm.org/D129183
This commit refactors the syntax of "ugly" attribute/type formats to not use
strings for wrapping. This means that moving forward attirbutes and type formats
will always need to be in some recognizable form, i.e. if they use incompatible
characters they will need to manually wrap those in a string, the framework will
no longer do it automatically.
This has the benefit of greatly simplifying how parsing attributes/types work, given
that we currently rely on some extremely complicated nested parser logic which is
quite problematic for a myriad of reasons; unecessary complexity(we create a nested
source manager/lexer/etc.), diagnostic locations can be off/wrong given string escaping,
etc.
Differential Revision: https://reviews.llvm.org/D118505
This commit enables support for providing and processing external
resources within MLIR assembly formats. This is a mechanism with which
dialects, and external clients, may attach additional information when
printing IR without that information being encoded in the IR itself.
External resources are not uniqued within the MLIR context, are not
attached directly to any operation, and are solely intended to live and be
processed outside of the immediate IR. There are many potential uses of this
functionality, for example MLIR's pass crash reproducer could utilize this to
attach the pass resource executing when a crash occurs. Other types of
uses may be embedding large amounts of binary data, such as weights in ML
applications, that shouldn't be copied directly into the MLIR context, but
need to be kept adjacent to the IR.
External resources are encoded using a key-value pair nested within a
dictionary anchored by name either on a dialect, or an externally registered
entity. The key is an identifier used to disambiguate the data. The value
may be stored in various limited forms, but general encodings use a string
(human readable) or blob format (binary). Within the textual format, an
example may be of the form:
```mlir
{-#
// The `dialect_resources` section within the file-level metadata
// dictionary is used to contain any dialect resource entries.
dialect_resources: {
// Here is a dictionary anchored on "foo_dialect", which is a dialect
// namespace.
foo_dialect: {
// `some_dialect_resource` is a key to be interpreted by the dialect,
// and used to initialize/configure/etc.
some_dialect_resource: "Some important resource value"
}
},
// The `external_resources` section within the file-level metadata
// dictionary is used to contain any non-dialect resource entries.
external_resources: {
// Here is a dictionary anchored on "mlir_reproducer", which is an
// external entity representing MLIR's crash reproducer functionality.
mlir_reproducer: {
// `pipeline` is an entry that holds a crash reproducer pipeline
// resource.
pipeline: "func.func(canonicalize,cse)"
}
}
```
Differential Revision: https://reviews.llvm.org/D126446
This is a full audit of emitError calls, I took the opportunity
to remove extranous parens and fix a couple cases where we'd
generate multiple diagnostics for the same error.
Differential Revision: https://reviews.llvm.org/D125355
This has a few benefits:
* It allows for defining parsers/printer code blocks that
can be shared between operations and attribute/types.
* It removes the weird duplication of generic parser/printer hooks,
which means that newly added hooks only require touching one class.
Differential Revision: https://reviews.llvm.org/D110375
DialectAsmParser::parseKeyword is rejecting `'i' digit+` while it is
a valid identifier according to mlir/docs/LangRef.md.
Integer types actually used to be TOK_KEYWORD a while back before the
change: 6af866c58d.
This patch Modifies `isCurrentTokenAKeyword` to return true for tokens that
match integer types too.
The motivation for this change is the parsing of `!fir.type<{` `component-name: component-type,`+ `}>`
type in FIR that represent Fortran derived types. The component-names are
parsed as keywords, and can very well be i32 or any ixxx (which are
valid Fortran derived type component names).
The Quant dialect type parser had to be modified since it relied on `iw` not
being parsed as keywords.
Differential Revision: https://reviews.llvm.org/D108913
This allows for parsing strings that have escape sequences, which require constructing
a string (as they can't be represented by looking at the Token contents directly).
Differential Revision: https://reviews.llvm.org/D108589
OpAsmParser (and DialectAsmParser) supports a pair of
parseInteger/parseOptionalInteger methods, which allow parsing a bare
integer into a C type of your choice (e.g. int8_t) using templates. It
was implemented in terms of a virtual method call that is hard coded to
int64_t because "that should be big enough".
Change the virtual method hook to return an APInt instead. This allows
asmparsers for custom ops to parse large integers if they want to, without
changing any of the clients of the fixed size C API.
Differential Revision: https://reviews.llvm.org/D102120
This information isn't useful for general compilation, but is useful for building tools that process .mlir files. This class will be used in a followup to start building an LSP language server for MLIR.
Differential Revision: https://reviews.llvm.org/D100438
This allows custom types and attribute to parse a dimension list that
isn't necessarily terminated with `xtype`, for example something like:
#tf.shape<4x5>
Differential Revision: https://reviews.llvm.org/D100432
This has been a TODO for a while, and prevents breakages for attributes/types that contain floats that can't roundtrip outside of the hex format.
Differential Revision: https://reviews.llvm.org/D98808
This function simplifies calling the getChecked methods on Attributes and Types from within the parser, and removes any need to use `getEncodedSourceLocation` for these methods (by using an SMLoc instead). This is much more efficient than using an mlir::Location, as the encoding process to produce an mlir::Location is inefficient and undesirable for parsing (locations used during parsing should not persist afterwards unless otherwise necessary).
Differential Revision: https://reviews.llvm.org/D97900
This also exposed a bug in Dialect loading where it was not correctly identifying identifiers that had the dialect namespace as a prefix.
Differential Revision: https://reviews.llvm.org/D97431
`verifyConstructionInvariants` is intended to allow for verifying the invariants of an attribute/type on construction, and `getChecked` is intended to enable more graceful error handling aside from an assert. There are a few problems with the current implementation of these methods:
* `verifyConstructionInvariants` requires an mlir::Location for emitting errors, which is prohibitively costly in the situations that would most likely use them, e.g. the parser.
This creates an unfortunate code duplication between the verifier code and the parser code, given that the parser operates on llvm::SMLoc and it is an undesirable overhead to pre-emptively convert from that to an mlir::Location.
* `getChecked` effectively requires duplicating the definition of the `get` method, creating a quite clunky workflow due to the subtle different in its signature.
This revision aims to talk the above problems by refactoring the implementation to use a callback for error emission. Using a callback allows for deferring the costly part of error emission until it is actually necessary.
Due to the necessary signature change in each instance of these methods, this revision also takes this opportunity to cleanup the definition of these methods by:
* restructuring the signature of `getChecked` such that it can be generated from the same code block as the `get` method.
* renaming `verifyConstructionInvariants` to `verify` to match the naming scheme of the rest of the compiler.
Differential Revision: https://reviews.llvm.org/D97100
This better matches the rest of the infrastructure, is much simpler, and makes it easier to move these types to being declaratively specified.
Differential Revision: https://reviews.llvm.org/D93432
Some operations use integer literals as part of their custom format that don't necessarily map to an internal IntegerAttr. This revision exposes the same `parseInteger` functions as the DialectAsmParser to allow for these operations to parse integer literals without incurring the otherwise unnecessary roundtrip through IntegerAttr.
Differential Revision: https://reviews.llvm.org/D93152
This is part of a larger refactoring the better congregates the builtin structures under the BuiltinDialect. This also removes the problematic "standard" naming that clashes with the "standard" dialect, which is not defined within IR/. A temporary forward is placed in StandardTypes.h to allow time for downstream users to replaced references.
Differential Revision: https://reviews.llvm.org/D92435
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
registry.insert<mlir::standalone::StandaloneDialect>();
registry.insert<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally
registered dialects on construction. Instead Dialects are only loaded explicitly
on demand:
- the Parser is lazily loading Dialects in the context as it encounters them
during parsing. This is the only purpose for registering dialects and not load
them in the context.
- Passes are expected to declare the dialects they will create entity from
(Operations, Attributes, or Types), and the PassManager is loading Dialects into
the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only
need to load the dialect for the IR it will emit, and the optimizer is
self-contained and load the required Dialects. For example in the Toy tutorial,
the compiler only needs to load the Toy dialect in the Context, all the others
(linalg, affine, std, LLVM, ...) are automatically loaded depending on the
optimization pipeline enabled.
To adjust to this change, stop using the existing dialect registration: the
global registry will be removed soon.
1) For passes, you need to override the method:
virtual void getDependentDialects(DialectRegistry ®istry) const {}
and registery on the provided registry any dialect that this pass can produce.
Passes defined in TableGen can provide this list in the dependentDialects list
field.
2) For dialects, on construction you can register dependent dialects using the
provided MLIRContext: `context.getOrLoadDialect<DialectName>()`
This is useful if a dialect may canonicalize or have interfaces involving
another dialect.
3) For loading IR, dialect that can be in the input file must be explicitly
registered with the context. `MlirOptMain()` is taking an explicit registry for
this purpose. See how the standalone-opt.cpp example is setup:
mlir::DialectRegistry registry;
mlir::registerDialect<mlir::standalone::StandaloneDialect>();
mlir::registerDialect<mlir::StandardOpsDialect>();
Only operations from these two dialects can be in the input file. To include all
of the dialects in MLIR Core, you can populate the registry this way:
mlir::registerAllDialects(registry);
4) For `mlir-translate` callback, as well as frontend, Dialects can be loaded in
the context before emitting the IR: context.getOrLoadDialect<ToyDialect>()
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
Differential Revision: https://reviews.llvm.org/D85622
This changes the behavior of constructing MLIRContext to no longer load globally registered dialects on construction. Instead Dialects are only loaded explicitly on demand:
- the Parser is lazily loading Dialects in the context as it encounters them during parsing. This is the only purpose for registering dialects and not load them in the context.
- Passes are expected to declare the dialects they will create entity from (Operations, Attributes, or Types), and the PassManager is loading Dialects into the Context when starting a pipeline.
This changes simplifies the configuration of the registration: a compiler only need to load the dialect for the IR it will emit, and the optimizer is self-contained and load the required Dialects. For example in the Toy tutorial, the compiler only needs to load the Toy dialect in the Context, all the others (linalg, affine, std, LLVM, ...) are automatically loaded depending on the optimization pipeline enabled.
The current modeling of LLVM IR types in MLIR is based on the LLVMType class
that wraps a raw `llvm::Type *` and delegates uniquing, printing and parsing to
LLVM itself. This model makes thread-safe type manipulation hard and is being
progressively replaced with a cleaner MLIR model that replicates the type
system. Introduce a set of classes reflecting the LLVM IR type system in MLIR
instead of wrapping the existing types. These are currently introduced as
separate classes without affecting the dialect flow, and are exercised through
a test dialect. Once feature parity is reached, the old implementation will be
gradually substituted with the new one.
Depends On D84171
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D84339
Summary: At this point Parser has grown to be over 5000 lines and can be very difficult to navigate/update/etc. This commit splits Parser.cpp into several sub files focused on parsing specific types of entities; e.g., Attributes, Types, etc.
Differential Revision: https://reviews.llvm.org/D81299
