Examle:
substitute
mesh.cluster @mesh0(rank = 2, dim_sizes = [0, 4])
with
mesh.cluster @mesh0(rank = 2, dim_sizes = ?x4)
Same as tensor/memref shapes. The only difference is for 0-rank shapes.
With tensors you would have something like `tensor<f32>`. Here to avoid
matching an empty string a 0-rank shape is denoted by `[]`.
MLIR can't really be const-correct (it would need a `ConstValue` class
alongside the `Value` class really, like `ArrayRef` and
`MutableArrayRef`). This is however making is more consistent: method
that are directly modifying the Value shouldn't be marked const.
system_endianness() just returns llvm::endianness::native, a
compile-time constant equivalent to std::native in C++20. This patch
deprecates system_endianness() while replacing all invocations of
system_endianness() with llvm::endianness::native.
While we are at it, this patch replaces
llvm::support::endianness::{big,little} with
llvm::endianness::{big,little} in those statements that happen to call
system_endianness(). It does not go out of its way to replace other
occurrences of llvm::support::endianness::{big,little}.
The current printer of `StringRefParameter` simply prints out the
content of the string as is without escaping it any way. This leads to
it generating invalid syntax, causing parser errors when read in again.
This PR fixes that by adding `printString` to `AsmPrinter`, allowing one
to print a string that can be parsed with `parseString`, using the same
escaping syntax as `StringAttr`.
Printing cyclic attributes and types currently has no first-class
support within the AsmPrinter and AsmParser. The workaround for this
issue used in all mutable attributes and types upstream has been to
create a `thread_local static SetVector` keeping track of currently
parsed and printed attributes.
This solution is not ideal readability wise due to the use of globals
and keeping track of state. Worst of all, this pattern had to be
reimplemented for every mutable attribute and type.
This patch therefore adds support for this pattern in `AsmPrinter` and
`AsmParser` replacing the use of this pattern. By calling
`tryStartCyclingPrint/Parse`, the mutable attribute or type are
registered in an internal stack. All subsequent calls to the function
with the same attribute or type will lead to returning failure. This way
the nesting can be detected and a short form printed or parsed instead.
Through the resetter returned by the call, the cyclic printing or
parsing region automatically ends on return.
The alias initializer keeps a list of child indices around. When an alias is then marked as non-deferrable, all children are also marked non-deferrable.
This is currently done naively which leads to an infinite recursion if using mutable types or attributes containing a cycle.
This patch fixes this by adding an early return if the alias is already marked non-deferrable. Since this function is the only way to mark an alias as non-deferrable, it is guaranteed that if it is marked non-deferrable, all its children are as well, and it is not required to walk all the children.
This incidentally makes the non-deferrable marking also `O(n)` instead of `O(n^2)` (although not performance sensitive obviously).
Differential Revision: https://reviews.llvm.org/D158932
In https://reviews.llvm.org/D157928 ellison of printing resources was added.
In the refactor, the proper printing of escape characters was mistakenly removed.
This patch adds it back in and adds a small unit test.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D158700
Often times, large weights for ML models will be stored as resources in MLIR. It is sometimes advantageous to control whether to print these resources for debugging purposes. For example, some models contain very big weights with millions of characters in printed size, which may slow down whatever text editor you are using.
This diff adds a flag which allows users to disable printing resources in these scenarios.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D157928
SmallVector<*, 0> is often a better replacement for std::vector :
both the object size and the code size are smaller.
(SmallMapVector uses SmallVector as well, but it is not common.)
clang size decreases by 0.0226%.
instructions:u decreases 0.037% when compiling a sqlite3 amalgram.
Reviewed By: JDevlieghere
Differential Revision: https://reviews.llvm.org/D156016
In cases where memory is of less of a concern (e.g. small attributes where all instances have to be distinct by definition), using `DistinctAttr` with a unit attribute is a useful and conscious way of generating deterministic unique IDs.
The syntax as is however, makes them less useful to use, as it 1) always prints `<unit>` at the back and 2) always aliases them leading to not very useful `#distinct = distinct[n]<unit>` lines in the printer output.
This patch fixes that by special casing `UnitAttr` to simply elide the `unit` attribute in the back and not printing it as alias in that case.
Differential Revision: https://reviews.llvm.org/D155162
A distinct attribute associates a referenced attribute with a unique
identifier. Every call to its create function allocates a new
distinct attribute instance. The address of the attribute instance
temporarily serves as its unique identifier. Similar to the names
of SSA values, the final unique identifiers are generated during
pretty printing.
Examples:
#distinct = distinct[0]<42.0 : f32>
#distinct1 = distinct[1]<42.0 : f32>
#distinct2 = distinct[2]<array<i32: 10, 42>>
This mechanism is meant to generate attributes with a unique
identifier, which can be used to mark groups of operations
that share a common properties such as if they are aliasing.
The design of the distinct attribute ensures minimal memory
footprint per distinct attribute since it only contains a reference
to another attribute. All distinct attributes are stored outside of
the storage uniquer in a thread local store that is part of the
context. It uses one bump pointer allocator per thread to ensure
distinct attributes can be created in-parallel.
Reviewed By: rriddle, Dinistro, zero9178
Differential Revision: https://reviews.llvm.org/D153360
At the moment, only the trailing dimensions in the vector type can be
scalable, i.e. this is supported:
vector<2x[4]xf32>
and this is not allowed:
vector<[2]x4xf32>
This patch extends the vector type so that arbitrary dimensions can be
scalable. To this end, an array of bool values is added to every vector
type to denote whether the corresponding dimensions are scalable or not.
For example, for this vector:
vector<[2]x[3]x4xf32>
the following array would be created:
{true, true, false}.
Additionally, the current syntax:
vector<[2x3]x4xf32>
is replaced with:
vector<[2]x[3]x4xf32>
This is primarily to simplify parsing (this way, the parser can easily
process one dimension at a time rather than e.g. tracking whether
"scalable block" has been entered/left).
NOTE: The `isScalableDim` parameter of `VectorType` (introduced in this
patch) makes `numScalableDims` redundant. For the time being,
`numScalableDims` is preserved to facilitate the transition between the
two parameters. `numScalableDims` will be removed in one of the
subsequent patches.
This change is a part of a larger effort to enable scalable
vectorisation in Linalg. See this RFC for more context:
* https://discourse.llvm.org/t/rfc-scalable-vectorisation-in-linalg/
Differential Revision: https://reviews.llvm.org/D153372
The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.
Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.
Context:
- https://mlir.llvm.org/deprecation/ at "Use the free function variants
for dyn_cast/cast/isa/…"
- Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443
Implementation:
This patch updates all remaining uses of the deprecated functionality in
mlir/. This was done with clang-tidy as described below and further
modifications to GPUBase.td and OpenMPOpsInterfaces.td.
Steps are described per line, as comments are removed by git:
0. Retrieve the change from the following to build clang-tidy with an
additional check:
main...tpopp:llvm-project:tidy-cast-check
1. Build clang-tidy
2. Run clang-tidy over your entire codebase while disabling all checks
and enabling the one relevant one. Run on all header files also.
3. Delete .inc files that were also modified, so the next build rebuilds
them to a pure state.
```
ninja -C $BUILD_DIR clang-tidy
run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
-header-filter=mlir/ mlir/* -fix
rm -rf $BUILD_DIR/tools/mlir/**/*.inc
```
Differential Revision: https://reviews.llvm.org/D151542
Running:
MLIR_OPT_CHECK_IR_ROUNDTRIP=1 ninja check-mlir
will now exercises all of our test with a round-trip to bytecode and a comparison for equality.
Reviewed By: rriddle, ftynse, jpienaar
Differential Revision: https://reviews.llvm.org/D90088
The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.
Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.
Context:
* https://mlir.llvm.org/deprecation/ at "Use the free function variants for dyn_cast/cast/isa/…"
* Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443
Implementation:
This follows a previous patch that updated calls
`op.cast<T>()-> cast<T>(op)`. However some cases could not handle an
unprefixed `cast` call due to occurrences of variables named cast, or
occurring inside of class definitions which would resolve to the method.
All C++ files that did not work automatically with `cast<T>()` are
updated here to `llvm::cast` and similar with the intention that they
can be easily updated after the methods are removed through a
find-replace.
See https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check
for the clang-tidy check that is used and then update printed
occurrences of the function to include `llvm::` before.
One can then run the following:
```
ninja -C $BUILD_DIR clang-tidy
run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
-export-fixes /tmp/cast/casts.yaml mlir/*\
-header-filter=mlir/ -fix
rm -rf $BUILD_DIR/tools/mlir/**/*.inc
```
Differential Revision: https://reviews.llvm.org/D150348
There was a discrepancy where the flag was honored when passed through the
command line, but not when passed through the API, which was leading to a
python test failing.
This new features enabled to dedicate custom storage inline within operations.
This storage can be used as an alternative to attributes to store data that is
specific to an operation. Attribute can also be stored inside the properties
storage if desired, but any kind of data can be present as well. This offers
a way to store and mutate data without uniquing in the Context like Attribute.
See the OpPropertiesTest.cpp for an example where a struct with a
std::vector<> is attached to an operation and mutated in-place:
struct TestProperties {
int a = -1;
float b = -1.;
std::vector<int64_t> array = {-33};
};
More complex scheme (including reference-counting) are also possible.
The only constraint to enable storing a C++ object as "properties" on an
operation is to implement three functions:
- convert from the candidate object to an Attribute
- convert from the Attribute to the candidate object
- hash the object
Optional the parsing and printing can also be customized with 2 extra
functions.
A new options is introduced to ODS to allow dialects to specify:
let usePropertiesForAttributes = 1;
When set to true, the inherent attributes for all the ops in this dialect
will be using properties instead of being stored alongside discardable
attributes.
The TestDialect showcases this feature.
Another change is that we introduce new APIs on the Operation class
to access separately the inherent attributes from the discardable ones.
We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`,
and other similar method which don't make the distinction explicit, leading
to an entirely separate namespace for discardable attributes.
Recommit d572cd1b06 after fixing python bindings build.
Differential Revision: https://reviews.llvm.org/D141742
This new features enabled to dedicate custom storage inline within operations.
This storage can be used as an alternative to attributes to store data that is
specific to an operation. Attribute can also be stored inside the properties
storage if desired, but any kind of data can be present as well. This offers
a way to store and mutate data without uniquing in the Context like Attribute.
See the OpPropertiesTest.cpp for an example where a struct with a
std::vector<> is attached to an operation and mutated in-place:
struct TestProperties {
int a = -1;
float b = -1.;
std::vector<int64_t> array = {-33};
};
More complex scheme (including reference-counting) are also possible.
The only constraint to enable storing a C++ object as "properties" on an
operation is to implement three functions:
- convert from the candidate object to an Attribute
- convert from the Attribute to the candidate object
- hash the object
Optional the parsing and printing can also be customized with 2 extra
functions.
A new options is introduced to ODS to allow dialects to specify:
let usePropertiesForAttributes = 1;
When set to true, the inherent attributes for all the ops in this dialect
will be using properties instead of being stored alongside discardable
attributes.
The TestDialect showcases this feature.
Another change is that we introduce new APIs on the Operation class
to access separately the inherent attributes from the discardable ones.
We envision deprecating and removing the `getAttr()`, `getAttrsDictionary()`,
and other similar method which don't make the distinction explicit, leading
to an entirely separate namespace for discardable attributes.
Differential Revision: https://reviews.llvm.org/D141742
This includes a small runtime acting as callback for the ExecutionEngine
and a C API that makes it possible to control from the debugger.
A python script for LLDB is included that hook a new `mlir` subcommand
and allows to set breakpoints and inspect the current action, the context
and the stack.
Differential Revision: https://reviews.llvm.org/D144817
X. Sun et al. (https://dl.acm.org/doi/10.5555/3454287.3454728) published
a paper showing that an FP format with 4 bits of exponent, 3 bits of
significand and an exponent bias of 11 would work quite well for ML
applications.
Google hardware supports a variant of this format where 0x80 is used to
represent NaN, as in the Float8E4M3FNUZ format. Just like the
Float8E4M3FNUZ format, this format does not support -0 and values which
would map to it will become +0.
This format is proposed for inclusion in OpenXLA's StableHLO dialect: https://github.com/openxla/stablehlo/pull/1308
As part of inclusion in that dialect, APFloat needs to know how to
handle this format.
Differential Revision: https://reviews.llvm.org/D146441
This is a convenient flag for context where we intend to summarize a top-level
operation without the full-blown regions it may hold.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D145889
Replace references to enumerate results with either result_pairs
(reference wrapper type) or structured bindings. I did not use
structured bindings everywhere as it wasn't clear to me it would
improve readability.
This is in preparation to the switch to zip semantics which won't
support non-const lvalue reference to elements:
https://reviews.llvm.org/D144503.
I chose to use values instead of const lvalue-refs because MLIR is
biased towards avoiding `const` local variables. This won't degrade
performance because currently `result_pair` is cheap to copy (size_t
+ iterator), and in the future, the enumerator iterator dereference
will return temporaries anyway.
Reviewed By: dblaikie
Differential Revision: https://reviews.llvm.org/D146006
This is a convenient flag for context where we intend to summarize a top-level
operation without the full-blown regions it may hold.
Differential Revision: https://reviews.llvm.org/D145889
Float8E5M2FNUZ and Float8E4M3FNUZ have been added to APFloat in D141863.
This change adds these types as MLIR builtin types alongside Float8E5M2
and Float8E4M3FN (added in D133823 and D138075).
Reviewed By: krzysz00
Differential Revision: https://reviews.llvm.org/D143744
This commit restructures the sub element infrastructure to be a core part
of attributes and types, instead of being relegated to an interface. This
establishes sub element walking/replacement as something "always there",
which makes it easier to rely on for correctness/etc (which various bits of
infrastructure want, such as Symbols).
Attribute/Type now have `walk` and `replace` methods directly
accessible, which provide power API for interacting with sub elements. As
part of this, a new AttrTypeWalker class is introduced that supports caching
walked attributes/types, and a friendlier API (see the simplification of symbol
walking in SymbolTable.cpp).
Differential Revision: https://reviews.llvm.org/D142272
This streamlines the implementation and makes it so that the virtual
tables are in the binary instead of dynamically assembled during initialization.
The dynamic allocation size of op registration is also smaller with this
change.
This reverts commit 7bf1e441da
and re-introduce e055aad5ff
after fixing the windows crash by making ParseAssemblyFn a
unique_function again
Differential Revision: https://reviews.llvm.org/D141492
This streamlines the implementation and makes it so that the virtual tables are in the binary instead of dynamically assembled during initialization.
The dynamic allocation size of op registration is also smaller with this
change.
Differential Revision: https://reviews.llvm.org/D141492
This patch removes the implementation of TypedAttr and ElementsAttr
from DenseArrayAttr and, in doing so, removes the need store a shaped
type. The attribute now stores a size (number of elements), an MLIR type
as a discriminator, and a raw byte array.
The intent of DenseArrayAttr was not to be a drop-in replacement for DenseElementsAttr. It was meant to be a simple container of integers or floats that map to C++ types. The ElementsAttr implementation on DenseArrayAttr had many holes in it, and fixing those holes would require evolving DenseArrayAttr in a way that is incompatible with its original purpose.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D137606
Calculating the position of the region trailing objects isn't free,
given that it's the last trailing object, and inlining the size check
removes the need for users to explicitly add size checks for
micro-optimization.
This commit refactors attribute/type alias generation to be similar to how
we do it for operations, i.e. we generate aliases determined on what is
actually necessary when printing the IR (using a dummy printer for alias
collection). This allows for generating aliases only when necessary, and
also allows for proper propagation of when a nested alias can be deferred.
This also necessitated a fix for location parsing to actually parse aliases
instead of ignoring them.
Fixes#59041
Differential Revision: https://reviews.llvm.org/D138886
