- introduce splat op in standard dialect (currently for int/float/index input
type, output type can be vector or statically shaped tensor)
- implement LLVM lowering (when result type is 1-d vector)
- add constant folding hook for it
- while on Ops.cpp, fix some stale names
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#141
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/141 from bondhugula:splat 48976a6aa0a75be6d91187db6418de989e03eb51
PiperOrigin-RevId: 270965304
The RFC for unifying Linalg and Affine compilation passes into an end-to-end flow with a predictable ABI and linkage to external function calls raised the question of why we have variable sized descriptors for memrefs depending on whether they have static or dynamic dimensions (https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
This CL standardizes the ABI on the rank of the memrefs.
The LLVM struct for a memref becomes equivalent to:
```
template <typename Elem, size_t Rank>
struct {
Elem *ptr;
int64_t sizes[Rank];
};
```
PiperOrigin-RevId: 270947276
This fixes a problem with current save-restore pattern of diagnostics handlers, as there may be a thread race between when the previous handler is destroyed. For example, this occurs when using multiple ParallelDiagnosticHandlers asynchronously:
Handler A
Handler B | - LifeTime - | Restore A here.
Handler C | --- LifeTime ---| Restore B after it has been destroyed.
The new design allows for multiple handlers to be registered in a stack like fashion. Handlers can return success() to signal that they have fully processed a diagnostic, or failure to propagate otherwise.
PiperOrigin-RevId: 270720625
- add more examples for affine layout maps showing various use
cases
- affine map range sizes were removed from code, but examples in
LangRef weren't updated
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#142
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/142 from bondhugula:doc 3291a8784bc69883f7a7cead21445fc8118aaad2
PiperOrigin-RevId: 270548991
Allow specification of decorators on SPIR-V StructType members. If the
struct has layout information, these decorations are to be specified
after the offset specification of the member. These decorations are
emitted as OpMemberDecorate instructions on the struct <id>. Update
(de)serialization to handle these decorations.
PiperOrigin-RevId: 270130136
Now that the pass manager is generalized, and nested/arbritrary pipelines are possible, it is important to document how to specify these types of pipelines from the command line.
PiperOrigin-RevId: 269207681
This doc serves as a manual for table-driven declarative rewrite rules.
It lists all the details regarding supported mechanisms.
PiperOrigin-RevId: 267761702
SPIR-V can explicitly declare structured control-flow constructs using merge
instructions. These explicitly declare a header block before the control
flow diverges and a merge block where control flow subsequently converges.
These blocks delimit constructs that must nest, and can only be entered
and exited in structured ways.
Instead of having a `spv.LoopMerge` op to directly model loop merge
instruction for indicating the merge and continue target, we use regions
to delimit the boundary of the loop: the merge target is the next op
following the `spv.loop` op and the continue target is the block that
has a back-edge pointing to the entry block inside the `spv.loop`'s region.
This way it's easier to discover all blocks belonging to a construct and
it plays nicer with the MLIR system.
Updated the SPIR-V.md doc.
PiperOrigin-RevId: 267431010
The syntax for splat attributes changed, but was not updated in the description
of the LLVM dialect constant operations in LLVM.md. Update the document to use
the correct syntax. Also add a dialect roundtrip test for such attribute,
which was previously missing.
PiperOrigin-RevId: 267116305
Some of the operations in the LLVM dialect are required to model the LLVM IR in
MLIR, for example "constant" operations are needed to declare a constant value
since MLIR, unlike LLVM, does not support immediate values as operands. To
avoid confusion with actual LLVM operations, we prefix such axuiliary
operations with "mlir.".
PiperOrigin-RevId: 266942838
This change generalizes the structure of the pass manager to allow arbitrary nesting pass managers for other operations, at any level. The only user visible change to existing code is the fact that a PassManager must now provide an MLIRContext on construction. A new class `OpPassManager` has been added that represents a pass manager on a specific operation type. `PassManager` will remain the top-level entry point into the pipeline, with OpPassManagers being nested underneath. OpPassManagers will still be implicitly nested if the operation type on the pass differs from the pass manager. To explicitly build a pipeline, the 'nest' methods on OpPassManager may be used:
// Pass manager for the top-level module.
PassManager pm(ctx);
// Nest a pipeline operating on FuncOp.
OpPassManager &fpm = pm.nest<FuncOp>();
fpm.addPass(...);
// Nest a pipeline under the FuncOp pipeline that operates on spirv::ModuleOp
OpPassManager &spvModulePM = pm.nest<spirv::ModuleOp>();
// Nest a pipeline on FuncOps inside of the spirv::ModuleOp.
OpPassManager &spvFuncPM = spvModulePM.nest<FuncOp>();
To help accomplish this a new general OperationPass is added that operates on opaque Operations. This pass can be inserted in a pass manager of any type to operate on any operation opaquely. An example of this opaque OperationPass is a VerifierPass, that simply runs the verifier opaquely on the current operation.
/// Pass to verify an operation and signal failure if necessary.
class VerifierPass : public OperationPass<VerifierPass> {
void runOnOperation() override {
Operation *op = getOperation();
if (failed(verify(op)))
signalPassFailure();
markAllAnalysesPreserved();
}
};
PiperOrigin-RevId: 266840344
The pass manager is moving towards being able to run on operations at arbitrary nesting. An operation may have both parent and child operations, and the AnalysisManager must be able to handle this generalization. The AnalysisManager class now contains generic 'getCachedParentAnalysis' and 'getChildAnalysis/getCachedChildAnalysis' functions to query analyses on parent/child operations. This removes the hard coded nesting relationship between Module/Function.
PiperOrigin-RevId: 266003636
The code and documentation for this chapter of the tutorial have been updated to follow the new flow. The toy 'array' type has been replaced by usages of the MLIR tensor type. The code has also been cleaned up and modernized.
Closestensorflow/mlir#101
PiperOrigin-RevId: 265744086
Change the use of 'array' to 'tensor' to reflect the new flow that the tutorial will follow. Also tidy up some of the documentation, code comments, and fix a few out-dated links.
PiperOrigin-RevId: 265174676
* Add a section on dialect attribute values and attribute aliases
* Move FloatAttr into its alphabetically correct place
* Add a "Standard Attribute Values" section
PiperOrigin-RevId: 264959306
* Alphabetize the type definitions
* Make 'Dialect specific types' a type-system subsection
* Merge Builtin types and Standard types
PiperOrigin-RevId: 264947721
Both sections are out-of-date and need to be updated. The dialect section is particularly bad in that it never actually mentions what a 'Dialect' is.
PiperOrigin-RevId: 264937905
Operation interfaces generally require a bit of boilerplate code to connect all of the pieces together. This cl introduces mechanisms in the ODS to allow for generating operation interfaces via the 'OpInterface' class.
Providing a definition of the `OpInterface` class will auto-generate the c++
classes for the interface. An `OpInterface` includes a name, for the c++ class,
along with a list of interface methods. There are two types of methods that can be used with an interface, `InterfaceMethod` and `StaticInterfaceMethod`. They are both comprised of the same core components, with the distinction that `StaticInterfaceMethod` models a static method on the derived operation.
An `InterfaceMethod` is comprised of the following components:
* ReturnType
- A string corresponding to the c++ return type of the method.
* MethodName
- A string corresponding to the desired name of the method.
* Arguments
- A dag of strings that correspond to a c++ type and variable name
respectively.
* MethodBody (Optional)
- An optional explicit implementation of the interface method.
def MyInterface : OpInterface<"MyInterface"> {
let methods = [
// A simple non-static method with no inputs.
InterfaceMethod<"unsigned", "foo">,
// A new non-static method accepting an input argument.
InterfaceMethod<"Value *", "bar", (ins "unsigned":$i)>,
// Query a static property of the derived operation.
StaticInterfaceMethod<"unsigned", "fooStatic">,
// Provide the definition of a static interface method.
// Note: `ConcreteOp` corresponds to the derived operation typename.
StaticInterfaceMethod<"Operation *", "create",
(ins "OpBuilder &":$builder, "Location":$loc), [{
return builder.create<ConcreteOp>(loc);
}]>,
// Provide a definition of the non-static method.
// Note: `op` corresponds to the derived operation variable.
InterfaceMethod<"unsigned", "getNumInputsAndOutputs", (ins), [{
return op.getNumInputs() + op.getNumOutputs();
}]>,
];
PiperOrigin-RevId: 264754898
The LangRef should contain documentation about the core system, and standard ops is a dialect just like any other. This will also simplify the transition when StandardOps is eventually split apart.
PiperOrigin-RevId: 264514988
Operation interfaces, as the name suggests, are those registered at the
Operation level. These interfaces provide an opaque view into derived
operations, by providing a virtual interface that must be implemented. As an
example, the Linalg dialect implements an interface LinalgOp that provides
general queries about some of the dialects library operations. These queries may
provide things like: the number of parallel loops, the number of inputs and
outputs, etc.
Operation interfaces are defined by overriding the CRTP base class OpInterface.
This class takes as a template parameter, a `Traits` class that defines a
Concept and a Model class. These classes provide an implementation of
concept-based polymorphism, where the Concept defines a set of virtual methods
that are overridden by the Model that is templated on the concrete operation
type. It is important to note that these classes should be pure in that they
contain no non-static data members.
PiperOrigin-RevId: 264218741
Switch to C++14 standard method as llvm::make_unique has been removed (
https://reviews.llvm.org/D66259). Also mark some targets as c++14 to ease next
integrates.
PiperOrigin-RevId: 263953918
Dialect interfaces are virtual apis registered to a specific dialect instance. Dialect interfaces are generally useful for transformation passes, or analyses, that want to opaquely operate on operations within a given dialect. These interfaces generally involve wide coverage over the entire dialect.
A dialect interface can be defined by inheriting from the CRTP base class DialectInterfaceBase::Base. This class provides the necessary utilities for registering an interface with the dialect so that it can be looked up later. Dialects overriding an interface may register an instance via 'Dialect::addInterfaces'. This API works very similarly to the respective addOperations/addTypes/etc. This will allow for a transformation/utility to later query the interface from an opaque dialect instance via 'getInterface<T>'.
A utility class 'DialectInterfaceCollection' is also provided that will collect all of the dialects that implement a specific interface within a given module. This allows for simplifying the API of interface lookups.
PiperOrigin-RevId: 263489015
This instruction is a local counterpart of llvm.global that takes a symbol
reference to a global and produces an SSA value containing the pointer to it.
Used in combination, these two operations allow one to use globals with other
operations expecting SSA values. At a cost of IR indirection, we make sure the
functions don't implicitly capture the surrounding SSA values and remain
suitable for parallel processing.
PiperOrigin-RevId: 262908622
This CL is step 3/n towards building a simple, programmable and portable vector abstraction in MLIR that can go all the way down to generating assembly vector code via LLVM's opt and llc tools.
This CL adds support for converting MLIR n-D vector types to (n-1)-D arrays of 1-D LLVM vectors and a conversion VectorToLLVM that lowers the `vector.extractelement` and `vector.outerproduct` instructions to the proper mix of `llvm.vectorshuffle`, `llvm.extractelement` and `llvm.mulf`.
This has been independently verified to produce proper avx2 code.
Input:
```
func @vec_1d(%arg0: vector<4xf32>, %arg1: vector<8xf32>) -> vector<8xf32> {
%2 = vector.outerproduct %arg0, %arg1 : vector<4xf32>, vector<8xf32>
%3 = vector.extractelement %2[0 : i32]: vector<4x8xf32>
return %3 : vector<8xf32>
}
```
Command:
```
mlir-opt vector-to-llvm.mlir -vector-lower-to-llvm-dialect --disable-pass-threading | mlir-opt -lower-to-cfg -lower-to-llvm | mlir-translate --mlir-to-llvmir | opt -O3 | llc -O3 -march=x86-64 -mcpu=haswell -mattr=fma,avx2
```
Output:
```
vec_1d: # @vec_1d
# %bb.0:
vbroadcastss %xmm0, %ymm0
vmulps %ymm1, %ymm0, %ymm0
retq
```
PiperOrigin-RevId: 262895929
Unlike regular constant values, strings must be placed in some memory and
referred to through a pointer to that memory. Until now, they were not
supported in function-local constant declarations with `llvm.constant`.
Introduce support for global strings using `llvm.global`, which would translate
them into global arrays in LLVM IR and thus make sure they have some memory
allocated for storage.
PiperOrigin-RevId: 262569316
Introduce an operation that defines global constants and variables in the LLVM
dialect, to reflect the corresponding LLVM IR capability. This operation is
expected to live in the top-level module and behaves similarly to
llvm.constant. It currently does not model many of the attributes supported by
the LLVM IR for global values (memory space, alignment, thread-local, linkage)
and will be extended as the relevant use cases appear.
PiperOrigin-RevId: 262539445
This CL modifies the LowerLinalgToLoopsPass to use RewritePattern.
This will make it easier to inline Linalg generic functions and regions when emitting to loops in a subsequent CL.
PiperOrigin-RevId: 261894120
This functionality was added recently and is intended to ensure that parametric
passes can be configured programmatically and not only from command-line flags,
which are mostly useless outside of standalone mlir-opt biary.
PiperOrigin-RevId: 261320932
MLIR does not have support for parsing special floating point values such as
infinities and NaNs. If programmatically constructed, these values are printed
as NaN and (+-)Inf and cannot be parsed back. Add parser support for
hexadecimal literals in float attributes, following LLVM IR. The literal
corresponds to the in-memory representation of the floating point value.
IEEE 754 defines a range of possible values for NaNs, storing the bitwise
representation allows MLIR to properly roundtrip NaNs with different bit values
of significands.
The initial version of this commit was missing support for float literals that
used to be printed in decimal notation as a fallback, but ended up being
printed in hexadecimal format which became the fallback for special values.
The decimal fallback behavior was not exercised by tests. It is currently
reinstated and tested by the newly added test @f32_potential_precision_loss in
parser.mlir.
PiperOrigin-RevId: 260790900
MLIR does not have support for parsing special floating point values such as
infinities and NaNs. If programmatically constructed, these values are printed
as NaN and (+-)Inf and cannot be parsed back. Add parser support for
hexadecimal literals in float attributes, following LLVM IR. The literal
corresponds to the in-memory representation of the floating point value.
IEEE 754 defines a range of possible values for NaNs, storing the bitwise
representation allows MLIR to properly roundtrip NaNs with different bit values
of significands.
PiperOrigin-RevId: 260018802
As the number of contributors begins to scale, and the number of tests rise, it is important to detail the testing strategy in MLIR and best practices for writing those tests.
PiperOrigin-RevId: 258612585
This allows for the attribute to hold symbolic references to other operations than FuncOp. This also allows for removing the dependence on FuncOp from the base Builder.
PiperOrigin-RevId: 257650017
This is an important step in allowing for the top-level of the IR to be extensible. FuncOp and ModuleOp contain all of the necessary functionality, while using the existing operation infrastructure. As an interim step, many of the usages of Function and Module, including the name, will remain the same. In the future, many of these will be relaxed to allow for many different types of top-level operations to co-exist.
PiperOrigin-RevId: 256427100
As with Functions, Module will soon become an operation, which are value-typed. This eases the transition from Module to ModuleOp. A new class, OwningModuleRef is provided to allow for owning a reference to a Module, and will auto-delete the held module on destruction.
PiperOrigin-RevId: 256196193
Now that Locations are attributes, they have direct access to the MLIR context. This allows for simplifying error emission by removing unnecessary context lookups.
PiperOrigin-RevId: 255112791
The current syntax separates the name and value with ':', but ':' is already overloaded by several other things(e.g. trailing types). This makes the syntax difficult to parse in some situtations:
Old:
"foo: 10 : i32"
New:
"foo = 10 : i32"
PiperOrigin-RevId: 255097928
This is the standard syntax for types on operations, and is also already used by IntegerAttr and FloatAttr.
Example:
dense<5> : tensor<i32>
dense<[3]> : tensor<1xi32>
PiperOrigin-RevId: 255069157
This CL adds the basic SPIR-V serializer and deserializer for converting
SPIR-V module into the binary format and back. Right now only an empty
module with addressing model and memory model is supported; (de)serialize
other components will be added gradually with subsequent CLs.
The purpose of this library is to enable importing SPIR-V binary modules
to run transformations on them and exporting SPIR-V modules to be consumed
by execution environments. The focus is transformations, which inevitably
means changes to the binary module; so it is not designed to be a general
tool for investigating the SPIR-V binary module and does not guarantee
roundtrip equivalence (at least for now).
PiperOrigin-RevId: 254473019
https://www.khronos.org/registry/spir-v/specs/1.0/SPIRV.html#OpTypeImage.
Add new enums to describe Image dimensionality, Image Depth, Arrayed
information, Sampling, Sampler User information, and Image format.
Doesn's support the Optional Access qualifier at this stage
Fix Enum generator for tblgen to add "_" at the beginning if the enum
starts with a number.
PiperOrigin-RevId: 254091423
This name has caused some confusion because it suggests that it's running op verification (and that this verification isn't getting run by default).
PiperOrigin-RevId: 254035268
This is a direct modelling of SPIR-V's OpVariable. The custom assembly format
parsers/prints descriptor in a nicer way if presents. There are other common
decorations that can appear on variables like builtin, which can be supported
later.
This CL additionally deduplicates the parser/printer/verifier declaration
in op definitions by adding defaults to SPV_Op base.
by adding
PiperOrigin-RevId: 253828254
Enum attributes can be defined using `EnumAttr`, which requires all its cases
to be defined with `EnumAttrCase`. To facilitate the interaction between
`EnumAttr`s and their C++ consumers, add a new EnumsGen TableGen backend
to generate a few common utilities, including an enum class, `llvm::DenseMapInfo`
for the enum class, conversion functions from/to strings.
This is controlled via the `-gen-enum-decls` and `-gen-enum-defs` command-line
options of `mlir-tblgen`.
PiperOrigin-RevId: 252209623
When manipulating generic operations, such as in dialect conversion /
rewriting, it is often necessary to view a list of Values as operands to an
operation without creating the operation itself. The absence of such view
makes dialect conversion patterns, among others, to use magic numbers to obtain
specific operands from a list of rewritten values when converting an operation.
Introduce XOpOperandAdaptor classes that wrap an ArrayRef<Value *> and provide
accessor functions identical to those available in XOp. This makes it possible
for conversions to use these adaptors to address the operands with names rather
than rely on their position in the list. The adaptors are generated from ODS
together with the actual operation definitions.
This is another step towards making dialect conversion patterns specific for a
given operation.
Illustrate the approach on conversion patterns in the standard to LLVM dialect
conversion.
PiperOrigin-RevId: 251232899
* There is no longer a need to explicitly remap function attrs.
- This removes a potentially expensive call from the destructor of Function.
- This will enable some interprocedural transformations to now run intraprocedurally.
- This wasn't scalable and forces dialect defined attributes to override
a virtual function.
* Replacing a function is now a trivial operation.
* This is a necessary first step to representing functions as operations.
--
PiperOrigin-RevId: 249510802
Establish the following convention:
1. Container class types end in "Of" (e.g. TensorOf) and take a list of allowed types.
2. An X container where only a single type is allowed is called TypeX (e.g. I32Tensor).
3. An X container where any type is allowed is called AnyX (e.g. AnyTensor).
--
PiperOrigin-RevId: 249281018
Using ArrayRef introduces issues with the order of evaluation between a constructor and
the arguments of the subsequent calls to the `operator()`.
As a consequence the order of captures is not well-defined can go wrong with certain compilers (e.g. gcc-6.4).
This CL fixes the issue by using lambdas in lieu of ArrayRef.
--
PiperOrigin-RevId: 249114775
This reduces conflict between these and other type names, where we're moving towards "Of" indicating a container type containing certain types. It also better matches the "Neg" predicate modifier and generally is pretty understandable/readable for predicates.
--
PiperOrigin-RevId: 249076508
Previously we force the C++ namespaces to be `NS` if `SomeOp` is defined as
`NS_SomeOp`. This is too rigid as it does not support nested namespaces
well. This CL adds a "namespace" field into the Dialect class to allow
flexible namespaces.
--
PiperOrigin-RevId: 249064981
This CL turns the previous "Op Definition" doc into a manual for table-driven
op definition specification by fleshing out more details of existing mechanisms.
--
PiperOrigin-RevId: 248013274
Restructure the Regions section in LangRef to avoid having a wall of text and
reflect a recent evolution of the design. Unspecify region types, that are put
on hold until use cases arise.
Update the Rationale doc with a list of design decisions related to regions.
Separately list the design alternatives that were considered and discarded due
to the lack of existing use cases.
--
PiperOrigin-RevId: 247943144
OSS build was broken (missing CMakeLists.txt changes and compilation failures on Ubuntu)
Automated rollback of changelist 247564213.
PiperOrigin-RevId: 247713812
The idea is to lower `gpu.launch` operations into `gpu.launch_func` operations by outlining the kernel body into a function, which is closer to the NVVM model.
--
PiperOrigin-RevId: 246806890
This syntax removes boilerplate and verbose list of region arguments in the
header of the entry block. It groups operands into segments related to GPU
blocks, GPU threads as well as the operands that are forwarded to the kernel.
The two former segments are also used to give names to the region arguments
that are used for GPU blocks and threads inside the kernel body region.
--
PiperOrigin-RevId: 246792329
The generic form of operations currently supports optional regions to be
located after the operation type. As we are going to add a type to each
region in a leading position in the region syntax, similarly to functions, it
becomes ambiguous to have regions immediately after the operation type. Put
regions between operands the optional list of successors in the generic
operation syntax and wrap them in parentheses. The effect on the exisitng IR
syntax is minimal since only three operations (`affine.for`, `affine.if` and
`gpu.kernel`) currently use regions.
--
PiperOrigin-RevId: 246787087
Region is the generalization of a function body (a list of blocks forming a CFG) to be allowed to be enclosed inside any operation. This nesting of IR is already leveraged in the affine dialect to support `affine.for`, `affine.if`, and `gpu.launch` operations.
--
PiperOrigin-RevId: 246766830
Trying to activate both LLVM and MLIR passes in mlir-cpu-runner showed name collisions when registering pass names.
One possible way of disambiguating that should also work across dialects is to prepend the dialect name to the passes that specifically operate on that dialect.
With this CL, mlir-cpu-runner tests still run when both LLVM and MLIR passes are registered
--
PiperOrigin-RevId: 246539917
This is just a bare skeleton to start populating developer policies and
guidelines. LLVM side this would be multiple separate documents (coding
standard & programmer's manual) but starting with one and we can break it out
into multiple if the content so dictates.
--
PiperOrigin-RevId: 246433346
This CL implements the previously unsupported parsing for Range, View and Slice operations.
A pass is introduced to lower to the LLVM.
Tests are moved out of C++ land and into mlir/test/Examples.
This allows better fitting within standard developer workflows.
--
PiperOrigin-RevId: 245796600
Define a new dialect related to GPU kernels. Currently, it only contains a
single operation for launching a kernel on a three-dimensional grid of thread
blocks, following a model similar to that of CUDA. In particular, the body of
the kernel contains operations executed by each thread and uses region
arguments to accept thread and block identifiers (similar to how the loop body
region accepts the induction value).
--
PiperOrigin-RevId: 245713728
Instead, fold such operations. This way callers don't need to conditionally create cast operations depending on if a value already has the target type.
Also, introduce areCastCompatible to allow cast users to verify that the generated op will be valid before creating the operation.
TESTED with unit tests
--
PiperOrigin-RevId: 245606133
none-type ::= `none`
The `none` type is a unit type, i.e. a type with exactly one possible value, where its value does not have a defined dynamic representation.
--
PiperOrigin-RevId: 245599248
Add a tutorial document explaining how to define a conversion from the Linalg
dialect to the LLVM IR dialect, bypassing the Affine dialect. It defines a
dynamic representation for a range and a view for the sake of type conversion.
Operation conversion becomes straightforward given the dynamic representation.
The code in the tutorial is better structured and better document that what we
currently have in the example, which will be updated separately.
--
PiperOrigin-RevId: 245498394
A unit attribute is an attribute that represents a value of `unit` type. The
`unit` type allows only one value forming a singleton set. This attribute value
is used to represent attributes that only have meaning from their existence.
One example of such an attribute could be the `swift.self` attribute. This attribute indicates that a function parameter is the self/context
parameter. It could be represented as a boolean attribute(true or false), but a
value of false doesn't really bring any value. The parameter either is the
self/context or it isn't.
```mlir {.mlir}
// A unit attribute defined with the `unit` value specifier.
func @verbose_form(i1 {unitAttr : unit})
// A unit attribute can also be defined without the `unit` value specifier.
func @simple_form(i1 {unitAttr})
```
--
PiperOrigin-RevId: 245254045
other characters within the <>'s now that we can. This will allow quantized
types to use the pretty syntax (among others) after a few changes.
--
PiperOrigin-RevId: 243521268
This adds parsing, printing and some folding/canonicalization.
Also extends rewriting of subi %0, %0 to handle vectors and tensors.
--
PiperOrigin-RevId: 242448164
making the IR dumps much nicer.
This is part 2/3 of the path to making dialect types more nice. Part 3/3 will
slightly generalize the set of characters allowed in pretty types and make it
more principled.
--
PiperOrigin-RevId: 242249955
restricted grammar. This will make certain common types much easier to read.
This is part tensorflow/mlir#1 of 2, which allows us to accept the new syntax. Part 2 will
change the asmprinter to automatically use it when appropriate, which will
require updating a bunch of tests.
This is motivated by the EuroLLVM tutorial and cleaning up the LLVM dialect aesthetics a bit more.
--
PiperOrigin-RevId: 242234821
Remove undesigned/unimplemented operations: reshape and view.
Add new LangRefDeletions.md file in /experimental to store things removed from public LangRef.md
PiperOrigin-RevId: 242230200
Uday Bondhugula