This is part of an effort to migrate from llvm::Optional to
std::optional. This patch changes the way mlir-tblgen generates .inc
files, and modifies tests and documentation appropriately. It is a "no
compromises" patch, and doesn't leave the user with an unpleasant mix of
llvm::Optional and std::optional.
A non-trivial change has been made to ControlFlowInterfaces to split one
constructor into two, relating to a build failure on Windows.
See also: https://discourse.llvm.org/t/deprecating-llvm-optional-x-hasvalue-getvalue-getvalueor/63716
Signed-off-by: Ramkumar Ramachandra <r@artagnon.com>
Differential Revision: https://reviews.llvm.org/D138934
Currently, assemblyFormat `custom<A>($a) custom<B>($b)` has different spacing
if used for Ops vs Attrs/Types. Ops insert a space if needed before the custom directive,
while attributes and types do not.
This leads to the following two patterns in attributes / types:
```
# 1. Whitespace literal
let assemblyFormat = "... ` ` custom<A>($a)"
# 2. Custom printer code includes spacing
void printB(...) {
printer << ' ' << b;
}
```
Moving this spacing into the generated code allows for some cleanup in mlir and
improves the consistency of custom directives.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D138235
This reverts commit 4e6dab98e0cb60c635656a818062887d97c3ef5f.
Re-apply: D138988 after fixing error on windows. Remove test for boolean
attributes as it does not make sense to apply these constraints on
boolean array.
This reverts commit dd0de4dca92cd6affafb47f788b64e99187168f1.
Build on mlir-windows fails:
Step 6 (build-check-mlir-build-only) failure: build (failure)
C:\buildbot\mlir-x64-windows-ninja\build\tools\mlir\test\lib\Dialect\Test\TestOps.cpp.inc(928): error C2220: the following warning is treated as an error
C:\buildbot\mlir-x64-windows-ninja\build\tools\mlir\test\lib\Dialect\Test\TestOps.cpp.inc(928): warning C4804: '>': unsafe use of type 'bool' in operation
C:\buildbot\mlir-x64-windows-ninja\build\tools\mlir\test\lib\Dialect\Test\TestOps.cpp.inc(7419): warning C4804: '>': unsafe use of type 'bool' in operation
- `DenseArrayStrictlyPositive` all elements are required to be > 0.
Returns true if the range is empty.
- `DenseArrayNonNegative` all elements are required to be >= 0. Returns
true if the range is empty.
Both constraints will simplify verifier logic as we move from using `I64ArrayAttr` to `DenseI64ArrayAttr`.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D138988
This patch adds `parseBase64Bytes` to the parser. It attempts to avoid double-allocating the buffer by re-using the token's spelling directly and eliding the quotes if they exist. It also avoids extra allocations by using std::vector<char> in the API - something we should change when the llvm::decodeBase64 API changes.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D138090
Currently CSE does not support CSE of ops with regions. This patch
extends the CSE support to ops with a single region.
Differential Revision: https://reviews.llvm.org/D134306
Depends on D137857
[RFC: EnumAttr for iterator types in Linalg](https://discourse.llvm.org/t/rfc-enumattr-for-iterator-types-in-linalg/64535)
This affect touches and probably breaks most of the code that creates `linalg.generic`. A fix would be to replace calls to `getParallelIteratorTypeName/getReductionIteratorTypeName` with `mlir::utils::IteratorType::parallel/reduction` and types from `StringRef` to `mlir::utils::IteratorType`.
Due to limitations of tablegen, shared C++ definition of IteratorType enum lives in StructuredOpsUtils.td, but each dialect should have it's own EnumAttr wrapper. To avoid conflict, all enums in a dialect are put into a separate file with a separate tablegen rule.
Test dialect td files are refactored a bit.
Printed format of `linalg.generic` temporarily remains unchanged to avoid breaking code and tests in the same change.
Differential Revision: https://reviews.llvm.org/D137658
This is the second (and final) step of making "destination style" usable without depending on the Linalg dialect. (The first step was D135129.)
This change allows us to provide default bufferization implementations for all destination-style ops. It also allows us to simplify `TilingInterface`. (E.g., `getDestinationOperands` can be removed.)
Differential Revision: https://reviews.llvm.org/D136179
This diff causes the `tblgen`-erated print() function to skip printing a
`DefaultValuedAttr` attribute when the value is equal to the default.
This feature will reduce the amount of custom printing code that needs to be
written by users a relatively common scenario. As a motivating example, for the
fastmath flags in the LLVMIR dialect, we would prefer to print this:
```
%0 = llvm.fadd %arg0, %arg1 : f32
```
instead of this:
```
%0 = llvm.fadd %arg0, %arg1 {fastmathFlags = #llvm.fastmath<none>} : f32
```
This diff makes the handling of print functionality for default-valued attributes
standard.
This is an updated version of https://reviews.llvm.org/D135398, without the per-attribute bit to control printing.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D135993
Add support for default-valued attributes as optional-group anchors. The
attribute is considered present if it holds a non-default value.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D134993
`getIteratorTypesArray` should be used instead. It's a better substitute for all the current usages of the interface.
The current `ArrayAttr iterator_types()` has a few problems:
* It creates an assumption operation has iterators types as an attribute, but it's not always the case. Sometime iterator types can be inferred from other attribute, or they're just static.
* ArrayAttr is an obscure contained and required extracting values in the client code.
* Makes it hard to migrate iterator types from strings to enums ([RFC](https://discourse.llvm.org/t/rfc-enumattr-for-iterator-types-in-linalg/64535/9)).
Concrete ops, like `linalg.generic` will still have iterator types as an attribute if needed.
As a side effect, this change helps a bit with migration to prefixed accessors.
Differential Revision: https://reviews.llvm.org/D135765
This patch takes the first step towards a more principled modeling of undefined behavior in MLIR as discussed in the following discourse threads:
1. https://discourse.llvm.org/t/semantics-modeling-undefined-behavior-and-side-effects/4812
2. https://discourse.llvm.org/t/rfc-mark-tensor-dim-and-memref-dim-as-side-effecting/65729
This patch in particular does the following:
1. Introduces a ConditionallySpeculatable OpInterface that dynamically determines whether an Operation can be speculated.
2. Re-defines `NoSideEffect` to allow undefined behavior, making it necessary but not sufficient for speculation. Also renames it to `NoMemoryEffect`.
3. Makes LICM respect the above semantics.
4. Changes all ops tagged with `NoSideEffect` today to additionally implement ConditionallySpeculatable and mark themselves as always speculatable. This combined trait is named `Pure`. This makes this change NFC.
For out of tree dialects:
1. Replace `NoSideEffect` with `Pure` if the operation does not have any memory effects, undefined behavior or infinite loops.
2. Replace `NoSideEffect` with `NoSideEffect` otherwise.
The next steps in this process are (I'm proposing to do these in upcoming patches):
1. Update operations like `tensor.dim`, `memref.dim`, `scf.for`, `affine.for` to implement a correct hook for `ConditionallySpeculatable`. I'm also happy to update ops in other dialects if the respective dialect owners would like to and can give me some pointers.
2. Update other passes that speculate operations to consult `ConditionallySpeculatable` in addition to `NoMemoryEffect`. I could not find any other than LICM on a quick skim, but I could have missed some.
3. Add some documentation / FAQs detailing the differences between side effects, undefined behavior, speculatabilty.
Reviewed By: rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D135505
We often have constraints for array attributes that they are sorted
non-decreasing or strictly increasing. This change adds AttrConstraint classes
that support DenseArrayAttr for integer types.
Differential Revision: https://reviews.llvm.org/D134944
(Re-Apply with fixes to clang MicrosoftMangle.cpp)
This is a first step towards high level representation for fp8 types
that have been built in to hardware with near term roadmaps. Like the
BFLOAT16 type, the family of fp8 types are inspired by IEEE-754 binary
floating point formats but, due to the size limits, have been tweaked in
various ways in order to maximally use the range/precision in various
scenarios. The list of variants is small/finite and bounded by real
hardware.
This patch introduces the E5M2 FP8 format as proposed by Nvidia, ARM,
and Intel in the paper: https://arxiv.org/pdf/2209.05433.pdf
As the more conformant of the two implemented datatypes, we are plumbing
it through LLVM's APFloat type and MLIR's type system first as a
template. It will be followed by the range optimized E4M3 FP8 format
described in the paper. Since that format deviates further from the
IEEE-754 norms, it may require more debate and implementation
complexity.
Given that we see two parts of the FP8 implementation space represented
by these cases, we are recommending naming of:
* `F8M<N>` : For FP8 types that can be conceived of as following the
same rules as FP16 but with a smaller number of mantissa/exponent
bits. Including the number of mantissa bits in the type name is enough
to fully specify the type. This naming scheme is used to represent
the E5M2 type described in the paper.
* `F8M<N>F` : For FP8 types such as E4M3 which only support finite
values.
The first of these (this patch) seems fairly non-controversial. The
second is previewed here to illustrate options for extending to the
other known variant (but can be discussed in detail in the patch
which implements it).
Many conversations about these types focus on the Machine-Learning
ecosystem where they are used to represent mixed-datatype computations
at a high level. At that level (which is why we also expose them in
MLIR), it is important to retain the actual type definition so that when
lowering to actual kernels or target specific code, the correct
promotions, casts and rescalings can be done as needed. We expect that
most LLVM backends will only experience these types as opaque `I8`
values that are applicable to some instructions.
MLIR does not make it particularly easy to add new floating point types
(i.e. the FloatType hierarchy is not open). Given the need to fully
model FloatTypes and make them interop with tooling, such types will
always be "heavy-weight" and it is not expected that a highly open type
system will be particularly helpful. There are also a bounded number of
floating point types in use for current and upcoming hardware, and we
can just implement them like this (perhaps looking for some cosmetic
ways to reduce the number of places that need to change). Creating a
more generic mechanism for extending floating point types seems like it
wouldn't be worth it and we should just deal with defining them one by
one on an as-needed basis when real hardware implements a new scheme.
Hopefully, with some additional production use and complete software
stacks, hardware makers will converge on a set of such types that is not
terribly divergent at the level that the compiler cares about.
(I cleaned up some old formatting and sorted some items for this case:
If we converge on landing this in some form, I will NFC commit format
only changes as a separate commit)
Differential Revision: https://reviews.llvm.org/D133823
This is a first step towards high level representation for fp8 types
that have been built in to hardware with near term roadmaps. Like the
BFLOAT16 type, the family of fp8 types are inspired by IEEE-754 binary
floating point formats but, due to the size limits, have been tweaked in
various ways in order to maximally use the range/precision in various
scenarios. The list of variants is small/finite and bounded by real
hardware.
This patch introduces the E5M2 FP8 format as proposed by Nvidia, ARM,
and Intel in the paper: https://arxiv.org/pdf/2209.05433.pdf
As the more conformant of the two implemented datatypes, we are plumbing
it through LLVM's APFloat type and MLIR's type system first as a
template. It will be followed by the range optimized E4M3 FP8 format
described in the paper. Since that format deviates further from the
IEEE-754 norms, it may require more debate and implementation
complexity.
Given that we see two parts of the FP8 implementation space represented
by these cases, we are recommending naming of:
* `F8M<N>` : For FP8 types that can be conceived of as following the
same rules as FP16 but with a smaller number of mantissa/exponent
bits. Including the number of mantissa bits in the type name is enough
to fully specify the type. This naming scheme is used to represent
the E5M2 type described in the paper.
* `F8M<N>F` : For FP8 types such as E4M3 which only support finite
values.
The first of these (this patch) seems fairly non-controversial. The
second is previewed here to illustrate options for extending to the
other known variant (but can be discussed in detail in the patch
which implements it).
Many conversations about these types focus on the Machine-Learning
ecosystem where they are used to represent mixed-datatype computations
at a high level. At that level (which is why we also expose them in
MLIR), it is important to retain the actual type definition so that when
lowering to actual kernels or target specific code, the correct
promotions, casts and rescalings can be done as needed. We expect that
most LLVM backends will only experience these types as opaque `I8`
values that are applicable to some instructions.
MLIR does not make it particularly easy to add new floating point types
(i.e. the FloatType hierarchy is not open). Given the need to fully
model FloatTypes and make them interop with tooling, such types will
always be "heavy-weight" and it is not expected that a highly open type
system will be particularly helpful. There are also a bounded number of
floating point types in use for current and upcoming hardware, and we
can just implement them like this (perhaps looking for some cosmetic
ways to reduce the number of places that need to change). Creating a
more generic mechanism for extending floating point types seems like it
wouldn't be worth it and we should just deal with defining them one by
one on an as-needed basis when real hardware implements a new scheme.
Hopefully, with some additional production use and complete software
stacks, hardware makers will converge on a set of such types that is not
terribly divergent at the level that the compiler cares about.
(I cleaned up some old formatting and sorted some items for this case:
If we converge on landing this in some form, I will NFC commit format
only changes as a separate commit)
Differential Revision: https://reviews.llvm.org/D133823
The current generation is unsafe as it is evaluated during verify
invocation rather than during verifySymbolUses. Remove until this is
safely generated.
Differential Revision: https://reviews.llvm.org/D134558
This is the corresponding method to
`OpAsmParser::parseOptionalLocationSpecifier` that prints a location
`loc(...)` based on the op printing flags. Together, these two functions
allow propagating user-level location info outside of their usual spots.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D134910
This patch changes optional groups to allow anchors in the 'else'
element group. When printing, the optional condition is inverted to
decide which group to print. This is useful for parsing concrete
optional elements that don't have a `parseOptional*` method or some
other way to test whether it's present.
Depends on D133805
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D133812
`ArrayOfAttr` can be used to easily create an attribute that just
contains an array of something. The elements can be other attributes,
in which case the custom parsers and printers are invoked directly for
nice syntax, or any C++ type that supports parsing and printing, either
though custom `printer` and `parser` methods or `FieldParser`.
An array of integers:
```
def ArrayOfInts : ArrayOfAttr<Test_Dialect, "ArrayOfInts", "array_of_ints",
"int32_t">;
```
When embedded in an op's assembly format, it will look like
```
foo.ints value = [1, 2, 3]
```
An array of enums, when embedded in an op's assembly format, will look
like:
```
foo.enums value = [first, second, last]
```
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D133131
This patch makes parsing dense arrays with type elision work properly.
If a ranked tensor type is supplied to `parseAttribute` on a dense
array, the element type is skipped. Moreover, if type elision is set to
`AttrTypeElision::Must`, the element type is elided.
For example, this allows
```
memref.global @z : memref<3xi32> = array<1, 2, 3>
```
Fixes#57433
Depends on D132758
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D132964
This patch fixes issues with generating assembly format parsers for
operations that use the `operands` directive or which have unnamed
arguments or results.
This patch also fixes a function in `OpAsmParser` that always produced
an error when trying to resolve variadic operands with the same type.
Fixes#51841
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D131627
When using multiple variadic results of differing sizes, using `AttrSizedResultSegments` is currently a requirement. Unlike `AttrSizedOperandSegments` however, it is not created within the default builders created by tablegen. Instead, one has to explicitly add `DenseI32ArrayAttr:$result_segments_sizes` as argument and then also explicitly specify all the sizes when using the builder from C++.
This patch fixes that redundancy, by making the builder generate the attribute in similar fashion as it already does for `AttrSizedOperandSegments`. The sizes required are simply gathered from the result type arguments of the builder.
Differential Revision: https://reviews.llvm.org/D132656
There are several use cases where a destination style operation needs an interface
that contains a subset of the methods from LinalgStructuredInterface.
In this change, we move all such methods to a new interface, and add forwarding
methods to LinalgStructuredInterface to make the change the less invasive.
It may be possible to refactor the code later to get rid of (some or all) of the
forwarding methods.
This change also removes the cloneWithMapper interface methods, as it is not used anywhere.
RFC:
https://discourse.llvm.org/t/rfc-interface-for-destination-style-ops/64056
Differential Revision: https://reviews.llvm.org/D132125
Confined -> ConfinedAttr
AllAttrConstraintsOf -> AllOfAttr
To be in line with ConfinedType and AllOfType.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D131822
This reland includes changes to the Python bindings.
Switch variadic operand and result segment size attributes to use the
dense i32 array. Dense integer arrays were introduced primarily to
represent index lists. They are a better fit for segment sizes than
dense elements attrs.
Depends on D131801
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D131803
Switch variadic operand and result segment size attributes to use the
dense i32 array. Dense integer arrays were introduced primarily to
represent index lists. They are a better fit for segment sizes than
dense elements attrs.
Depends on D131738
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D131702
This patch adds a DenseI1ArrayAttr to support arrays of i1. Importantly,
the implementation is as a simple `ArrayRef<bool>` instead of using bit
compression, which was problematic in DenseElementsAttr.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D130957
This patch removes the `type` field from `Attribute` along with the
`Attribute::getType` accessor.
Going forward, this means that attributes in MLIR will no longer have
types as a first-class concept. This patch lays the groundwork to
incrementally remove or refactor code that relies on generic attributes
being typed. The immediate impact will be on attributes that rely on
`Attribute` containing a type, such as `IntegerAttr`,
`DenseElementsAttr`, and `ml_program::ExternAttr`, which will now need
to define a type parameter on their storage classes. This will save
memory as all other attribute kinds will no longer contain a type.
Moreover, it will not be possible to generically query the type of an
attribute directly. This patch provides an attribute interface
`TypedAttr` that implements only one method, `getType`, which can be
used to generically query the types of attributes that implement the
interface. This interface can be used to retain the concept of a "typed
attribute". The ODS-generated accessor for a `type` parameter
automatically implements this method.
Next steps will be to refactor the assembly formats of certain operations
that rely on `parseAttribute(type)` and `printAttributeWithoutType` to
remove special handling of type elision until `type` can be removed from
the dialect parsing hook entirely; and incrementally remove uses of
`TypedAttr`.
Reviewed By: lattner, rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D130092
Added a commutativity utility pattern and a function to populate it. The pattern sorts the operands of an op in ascending order of the "key" associated with each operand iff the op is commutative. This sorting is stable.
The function is intended to be used inside passes to simplify the matching of commutative operations. After the application of the above-mentioned pattern, since the commutative operands now have a deterministic order in which they occur in an op, the matching of large DAGs becomes much simpler, i.e., requires much less number of checks to be written by a user in her/his pattern matching function.
The "key" associated with an operand is the list of the "AncestorKeys" associated with the ancestors of this operand, in a breadth-first order.
The operand of any op is produced by a set of ops and block arguments. Each of these ops and block arguments is called an "ancestor" of this operand.
Now, the "AncestorKey" associated with:
1. A block argument is `{type: BLOCK_ARGUMENT, opName: ""}`.
2. A non-constant-like op, for example, `arith.addi`, is `{type: NON_CONSTANT_OP, opName: "arith.addi"}`.
3. A constant-like op, for example, `arith.constant`, is `{type: CONSTANT_OP, opName: "arith.constant"}`.
So, if an operand, say `A`, was produced as follows:
```
`<block argument>` `<block argument>`
\ /
\ /
`arith.subi` `arith.constant`
\ /
`arith.addi`
|
returns `A`
```
Then, the block arguments and operations present in the backward slice of `A`, in the breadth-first order are:
`arith.addi`, `arith.subi`, `arith.constant`, `<block argument>`, and `<block argument>`.
Thus, the "key" associated with operand `A` is:
```
{
{type: NON_CONSTANT_OP, opName: "arith.addi"},
{type: NON_CONSTANT_OP, opName: "arith.subi"},
{type: CONSTANT_OP, opName: "arith.constant"},
{type: BLOCK_ARGUMENT, opName: ""},
{type: BLOCK_ARGUMENT, opName: ""}
}
```
Now, if "keyA" is the key associated with operand `A` and "keyB" is the key associated with operand `B`, then:
"keyA" < "keyB" iff:
1. In the first unequal pair of corresponding AncestorKeys, the AncestorKey in operand `A` is smaller, or,
2. Both the AncestorKeys in every pair are the same and the size of operand `A`'s "key" is smaller.
AncestorKeys of type `BLOCK_ARGUMENT` are considered the smallest, those of type `CONSTANT_OP`, the largest, and `NON_CONSTANT_OP` types come in between. Within the types `NON_CONSTANT_OP` and `CONSTANT_OP`, the smaller ones are the ones with smaller op names (lexicographically).
---
Some examples of such a sorting:
Assume that the sorting is being applied to `foo.commutative`, which is a commutative op.
Example 1:
> %1 = foo.const 0
> %2 = foo.mul <block argument>, <block argument>
> %3 = foo.commutative %1, %2
Here,
1. The key associated with %1 is:
```
{
{CONSTANT_OP, "foo.const"}
}
```
2. The key associated with %2 is:
```
{
{NON_CONSTANT_OP, "foo.mul"},
{BLOCK_ARGUMENT, ""},
{BLOCK_ARGUMENT, ""}
}
```
The key of %2 < the key of %1
Thus, the sorted `foo.commutative` is:
> %3 = foo.commutative %2, %1
Example 2:
> %1 = foo.const 0
> %2 = foo.mul <block argument>, <block argument>
> %3 = foo.mul %2, %1
> %4 = foo.add %2, %1
> %5 = foo.commutative %1, %2, %3, %4
Here,
1. The key associated with %1 is:
```
{
{CONSTANT_OP, "foo.const"}
}
```
2. The key associated with %2 is:
```
{
{NON_CONSTANT_OP, "foo.mul"},
{BLOCK_ARGUMENT, ""}
}
```
3. The key associated with %3 is:
```
{
{NON_CONSTANT_OP, "foo.mul"},
{NON_CONSTANT_OP, "foo.mul"},
{CONSTANT_OP, "foo.const"},
{BLOCK_ARGUMENT, ""},
{BLOCK_ARGUMENT, ""}
}
```
4. The key associated with %4 is:
```
{
{NON_CONSTANT_OP, "foo.add"},
{NON_CONSTANT_OP, "foo.mul"},
{CONSTANT_OP, "foo.const"},
{BLOCK_ARGUMENT, ""},
{BLOCK_ARGUMENT, ""}
}
```
Thus, the sorted `foo.commutative` is:
> %5 = foo.commutative %4, %3, %2, %1
Signed-off-by: Srishti Srivastava <srishti.srivastava@polymagelabs.com>
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D124750
When this was updated in D127139 the update in-place case was no longer
marked as pessimistic. Add back in.
Differential Revision: https://reviews.llvm.org/D130453
This one required more changes than ideal due to overlapping generated name
with different return types. Changed getIndexingMaps to getIndexingMapsArray to
move it out of the way/highlight that it returns (more expensively) a
SmallVector and uses the prefixed name for the Attribute.
Differential Revision: https://reviews.llvm.org/D129919
In the current state, this is only special cased for Allocation effects, but any effects on results allocated by the operation may be ignored when checking whether the op may be removed, as none of them are possible to be observed if the result is unused.
A use case for this is for IRs for languages which always initialize on allocation. To correctly model such operations, a Write as well as an Allocation effect should be placed on the result. This would prevent the Op from being deleted if unused however. This patch fixes that issue.
Differential Revision: https://reviews.llvm.org/D129854
refineReturnType method shares the same parameters as inferReturnTypes
but gets passed in the return types of the op if known that can be used
during refinement passes or for more op specific error reporting.
Currently the error reporting on failure is generic and doesn't allow
for specializing the returned result based on failure, with this change
what would previously have been a separate trait with specialized
verification can just be handled as part of inferrence rather than
duplicated.
refineReturnTypes behaves like inferReturnTypes if no result types are fed in,
while the current verification is recast as the default implementation for
refineReturnTypes with it calling inferReturnTypes (and so the default type
verification now goes through refine and allows for more op specific inference
mismatch errors).
Differential Revision: https://reviews.llvm.org/D129955
This attribute is similar to DenseElementsAttr but does not support
splat. As such it has a much simpler API and does not need any smart
iterator: it exposes direct ArrayRef access.
A new syntax is introduced so that the generic printing/parsing looks
like:
[:i64 1, -2, 3]
This attribute beings like an ArrayAttr but has a `:` token after the
opening square brace to introduce the element type (supported are I8,
I16, I32, I64, F32, F64) and the comma separated list for the data.
This is particularly convenient for attributes intended to be small,
like those referring to shapes.
For example a `transpose` operation with a `dims` attribute could be
defined as such:
let arguments = (ins AnyTensor:$input, DenseI64ArrayAttr:$dims);
let assemblyFormat = "$input `dims` `=` $dims attr-dict : type($input)";
And printed this way (the element type is elided in this case):
transpose %input dims = [0, 2, 1] : tensor<2x3x4xf32>
The C++ API for dims would just directly return an ArrayRef<int64>
RFC: https://discourse.llvm.org/t/rfc-introduce-a-new-dense-array-attribute/63279
Recommit with a custom DenseArrayBaseAttrStorage class to ensure
over-alignment of the storage to the largest type.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D123774
This attribute is similar to DenseElementsAttr but does not support
splat. As such it has a much simpler API and does not need any smart
iterator: it exposes direct ArrayRef access.
A new syntax is introduced so that the generic printing/parsing looks
like:
[:i64 1, -2, 3]
This attribute beings like an ArrayAttr but has a `:` token after the
opening square brace to introduce the element type (supported are I8,
I16, I32, I64, F32, F64) and the comma separated list for the data.
This is particularly convenient for attributes intended to be small,
like those referring to shapes.
For example a `transpose` operation with a `dims` attribute could be
defined as such:
let arguments = (ins AnyTensor:$input, DenseI64ArrayAttr:$dims);
let assemblyFormat = "$input `dims` `=` $dims attr-dict : type($input)";
And printed this way (the element type is elided in this case):
transpose %input dims = [0, 2, 1] : tensor<2x3x4xf32>
The C++ API for dims would just directly return an ArrayRef<int64>
RFC: https://discourse.llvm.org/t/rfc-introduce-a-new-dense-array-attribute/63279
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D123774
Ops that implement `RegionBranchOpInterface` are allowed to indicate that they can branch back to themselves in `getSuccessorRegions`, but there is no API that allows them to specify the forwarded operands. This patch enables that by changing `getSuccessorEntryOperands` to accept `None`.
Fixes#54928
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D127239
This reverts commit 4e5ce2056e3e85f109a074e80bdd23a10ca2bed9.
This relands commit 1350c9887dca5ba80af8e3c1e61b29d6696eb240.
Reinstates the range analysis with the build issue fixed.
Differential Revision: https://reviews.llvm.org/D126926
