This patch introduces an implementation of dense data-flow analysis. Dense
data-flow analysis attaches a lattice before and after the execution of every
operation. The lattice state is propagated across operations by a user-defined
transfer function. The state is joined across control-flow and callgraph edges.
Thge patch provides an example pass that uses both a dense and a sparse analysis
together.
Depends on D127139
Reviewed By: rriddle, phisiart
Differential Revision: https://reviews.llvm.org/D127173
Since version 0.9 we've:
* Bumped the language-client to `8.0.2-next.5` to fix various bugs/stability issues
* Fixed an issue with starting a language server for non-workspace files
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 includes a fix for a code completion/document update bug where
code completion results were being requested before the document actually
updated.
Differential Revision: https://reviews.llvm.org/D129182
In the newer versions of the language client, this explicitly expects a
Promise<void> return type, otherwise it errors out.
Fixes#56297
Differential Revision: https://reviews.llvm.org/D129181
OpenCL's round function matches `math.round` so we can directly lower to
the op, this includes adding the op definition to the SPIRV OCL ops.
GLSL does not guarantee rounding direction so we include custom rounding
code to guarantee correct rounding direction.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D129236
This patch introduces a (forward) sparse data-flow analysis implemented with the data-flow analysis framework. The analysis interacts with liveness information that can be provided by dead-code analysis to be conditional. This patch re-implements SCCP using dead-code analysis and (conditional) constant propagation analyses.
Depends on D127064
Reviewed By: rriddle, phisiart
Differential Revision: https://reviews.llvm.org/D127139
Because the buffer descriptor structure (the V#) has no backwards-compatibility
guarentees, and since said guarantees have been violated in practice
(see https://github.com/llvm/llvm-project/issues/56323 ), and since
the `targetIsRDNA` attribute isn't something that higher-level clients can set
in general, make the lowering of the amdgpu dialect to rocdl take a --chipset
option.
Note that this option is a string because adding a parser for the Chipset
struct to llvm::cl wasn't working out.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D129228
This revision revisits the implementation of applyToOne and its handling
of recoverable errors as well as propagation of null handles.
The implementation is simplified to always require passing a vector<Operation*>
in which the results are returned, resulting in less template instantiation magic.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D129185
This patch refactors existing implementations of division representation storage
into a new class, DivisionRepr. This refactoring is done so that the common
division utilities can be shared in an upcoming patch.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D129146
The `unknownTypeConversion` bufferization option (enum) is now a type converter function option. Some logic of `getMemRefType` is now handled by that function.
This change makes type conversion more controllable. Previously, there were only two options when generating memref types for non-bufferizable ops: Static identity layout or fully dynamic layout. With this change, users of One-Shot Bufferize can provide a function with custom logic.
Differential Revision: https://reviews.llvm.org/D129273
This Transform dialect op allows one to merge the lists of Payload IR
operations pointed to by several handles into a single list associated with one
handle. This is an important Transform dialect usability improvement for cases
where transformations may temporarily diverge for different groups of Payload
IR ops before converging back to the same script. Without this op, several
copies of the trailing transformations would have to be present in the
transformation script.
Depends On D129090
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D129110
Introduce a new transformation on structured ops that splits the iteration
space into two parts along the specified dimension. The index at which the
splitting happens may be static or dynamic. This transformation can be seen as
a rudimentary form of index-set splitting that only supports the splitting
along hyperplanes parallel to the iteration space hyperplanes, and is therefore
decomposable into per-dimension application.
It is a key low-level transformation that enables independent scheduling for
different parts of the iteration space of the same op, which hasn't been
possible previously. It may be used to implement, e.g., multi-sized tiling. In
future, peeling can be implemented as a combination of split-off amount
computation and splitting.
The transformation is conceptually close to tiling in its separation of the
iteration and data spaces, but cannot be currently implemented on top of
TilingInterface as the latter does not properly support `linalg.index`
offsetting.
Note that the transformation intentionally bypasses folding of
`tensor.extract_slice` operations when creating them as this folding was found
to prevent repeated splitting of the same operation because due to internal
assumptions about extract/insert_slice combination in dialect utilities.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D129090
This patch makes use of TypeInterface implementing Type to remove instances of Type that simply checked whether a type implemented a given interface.
As part of this refactoring, some changes had to be done in the OpenMP Dialect files. In particular, they assumed that OpenMPOps.td to only ever include OpenMP TypeInterfaces, which did not hold anymore with a moved include in LLVMOpBase.td. For that reason, the type interface defintions were moved into a new file as is convention.
Differential Revision: https://reviews.llvm.org/D129210
By making TypeInterfaces and AttrInterfaces, Types and Attrs respectively it'd then be possible to use them anywhere where a Type or Attr may go. That is within the arguments and results of an Op definition, in a RewritePattern etc.
Prior to this change users had to separately define a Type or Attr, with a predicate to check whether a type or attribute implements a given interface. Such code will be redundant now.
Removing such occurrences in upstream dialects will be part of a separate patch.
As part of implementing this patch, slight refactoring had to be done. In particular, Interfaces cppClassName field was renamed to cppInterfaceName as it "clashed" with TypeConstraints cppClassName. In particular Interfaces cppClassName expected just the class name, without any namespaces, while TypeConstraints cppClassName expected a fully qualified class name.
Differential Revision: https://reviews.llvm.org/D129209
This patch adds a conditional binary constant folder which allow to exit when the constants not meet the fold condition. And use it for PowFOp to make it able to fold the constant dense.
Differential Revision: https://reviews.llvm.org/D129108
Per @rriddle, we do not want to require `skipDefaultBuilders=0` per se; that is, even though the `assemblyFormat`-generated parser requires a builder with the same prototype as the default-builder, that prototype could instead be implemented via custom `builders`. This differential reduces the FatalError introduced in D128555 to a non-fatal Warning instead, so that users can still be informed of the error condition (rather than waiting for the C++ compiler to fail).
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D129234
The revision makes a start with implementing expand/collapse reshaping
for sparse tensors. When either source or destination is sparse, but
other is dense, the "cheap" dense reshape can be used prior to converting
from or to a sparse tensor.
Note1
sparse to sparse reshaping is still TBD.
Note2
in the long run, we may want to implement a "view" into a sparse tensor so that the operation remains cheap and does not require data shuffling
Reviewed By: wrengr
Differential Revision: https://reviews.llvm.org/D129031
This patch implements a lexicographic max/min union of two PWMAFunctions.
The lexmax/lexmin union of two functions is defined as a function defined on
the union of the input domains of both functions, such that when only one of the
functions are defined, it outputs the same as that function, and if both are
defined, it outputs the lexmax/lexmin of the two outputs. On points where
neither function is defined, the union is not defined either.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D128829
Prior to this patch, using any kind of interface (op interface, attr interface, type interface) as the key of a llvm::DenseSet, llvm::DenseMap or other related containers, leads to invalid pointer dereferences, despite compiling.
The gist of the problem is that a llvm::DenseMapInfo specialization for the base type (aka one of Operation*, Type or Attribute) are selected when using an interface as a key, which uses getFromOpaquePointer with invalid pointer addresses to construct instances for the empty key and tombstone key values. The interface is then constructed with this invalid base type and an attempt is made to lookup the implementation in the interface map, which then dereferences the invalid pointer address. (For more details and the exact call chain involved see the GitHub issue below)
The current workaround is to use the more generic base type (eg. instead of DenseSet<FunctionOpInterface>, DenseSet<Operation*>), but this is strictly worse from a code perspective (doesn't enforce the invariant, code is less self documenting, having to insert casts etc).
This patch fixes that issue by defining a DenseMapInfo specialization of Interface subclasses which uses a new constructor to construct an instance without querying a concept. That allows getEmptyKey and getTombstoneKey to construct an interface with invalid pointer values.
Fixes https://github.com/llvm/llvm-project/issues/54908
Differential Revision: https://reviews.llvm.org/D129038
Previously, these warnings were seen:
[6599/7564] Building native mlir-pdll...
[270/278] Building CXX object tools/mlir/lib/Tools/PDLL/CodeGen/CMakeFiles/obj.MLIRPDLLCodeGen.dir/CPPGen.cpp.obj
In file included from C:/git/llvm-project/mlir/lib/Tools/PDLL/CodeGen/CPPGen.cpp:20:
C:/git/llvm-project/mlir/include\mlir/Tools/PDLL/ODS/Operation.h(202,16): warning: unqualified friend declaration referring to type outside of the nearest enclosing namespace is a Microsoft extension; add a nested name specifier [-Wmicrosoft-unqualified-friend]
friend class Dialect;
^
::mlir::
1 warning generated.
[278/278] Linking CXX executable bin\mlir-pdll.exe
[6857/7564] Building CXX object tools/mlir/unittests/ExecutionEngine/CMakeFiles/MLIRExecutionEngineTests.dir/Invoke.cpp.obj
In file included from C:/git/llvm-project/mlir/unittests/ExecutionEngine/Invoke.cpp:20:
C:/git/llvm-project/mlir/include\mlir/ExecutionEngine/MemRefUtils.h(104,27): warning: shift count >= width of type [-Wshift-count-overflow]
assert(sizeof(T) < (1ul << 32) && "Elemental type overflows");
^ ~~
C:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\ucrt\assert.h(40,17): note: expanded from macro 'assert'
(!!(expression)) || \
^~~~~~~~~~
1 warning generated.
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
With the exceptions of AttrOrTypeParameter and DerivedAttr, all of MLIR consistently uses $_ctxt as the substitute variable for the MLIRContext in TableGen C++ code.
Usually this does not matter unless one where to reuse some code in multiple fields but it is still needlessly inconsistent and prone to error.
This patch fixes that by consistently using _$ctxt everywhere.
Differential Revision: https://reviews.llvm.org/D129153
- Applying CallOpInterface on CallOp and InvokeOp.
- Applying CallableInterface on LLVMFuncOp.
We're testing the changes using CallGraph, which uses both interfaces.
Differential Revision: https://reviews.llvm.org/D129026
"attachment" was a temporary name chosen for the information attached to a
variable in a PresburgerSpace. After the disambiguation of "variables" and
"identifiers" in PresburgerSpace, we use the word "identifiers" for this
information, since this information is used to "identify" these variables.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D128751
The result shape of a rank-reducing subview cannot be inferred in the general case. Just the result rank is not enough. The only thing that we can infer is the layout map.
This change also improves the bufferization patterns of tensor.extract_slice and tensor.insert_slice to fully support rank-reducing operations.
Differential Revision: https://reviews.llvm.org/D129144
This patch extends the affine parser to allow affine constraints with `<=`.
This is useful in writing unittests for Presburger library and test in general.
The internal storage and printing of IntegerSet is still in the original format.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D129046
ParallelInsertSlice behaves similarly to tensor::InsertSliceOp in its
rank-reducing properties.
This revision extends rank-reducing rewrite behavior and reuses most of the
existing implementation.
Differential Revision: https://reviews.llvm.org/D129091
Infers block/grid dimensions/indices or ranges of such dimensions/indices.
Reviewed By: krzysz00
Differential Revision: https://reviews.llvm.org/D129036
This patch adds translation for omp.task from OpenMPDialect to LLVM IR
Dialect and adds tests for the same.
Depends on D71989
Reviewed By: ftynse, kiranchandramohan, peixin, Meinersbur
Differential Revision: https://reviews.llvm.org/D123919
The refactor in https://reviews.llvm.org/D128230 introduced a new target and the name is not scoped properly, leading to name collisions on larger projects. It is done properly on the target just below, so applying the same pattern here fixes the issue.
Currently, there've been a lot of warnings while building MLIR.
This change fixes the warnings listed below.
.../SparseTensorUtils.cpp: In instantiation of ‘...::openSparseTensorCOO(...) [with ...]’:
.../SparseTensorUtils.cpp:1672:3: required from here
.../SparseTensorUtils.cpp:87:21: warning: format ‘%d’ expects argument of type ‘int’, but argument 3 has type ‘PrimaryType’ [-Wformat=]
.../OptUtils.cpp:36:5: warning: this statement may fall through [-Wimplicit-fallthrough=]
.../AffineOps.cpp:1741:32: warning: suggest parentheses around ‘&&’ within ‘||’ [-Wparentheses]
Reviewed By: aartbik, wrengr, aeubanks
Differential Revision: https://reviews.llvm.org/D128993
This revision updates the op semantics to also allow rank-reducing behavior as well
as updates the implementation to reuse code between the sequential and the parallel
version of the op.
Depends on D128920
Differential Revision: https://reviews.llvm.org/D128985
This is moslty NFC and will allow tensor.parallel_insert_slice to gain
rank-reducing semantics by reusing the vast majority of the tensor.insert_slice impl.
Depends on D128857
Differential Revision: https://reviews.llvm.org/D128920
Currently, the parser for IntegerSet, only allows constraints like:
```
affine-constraint ::= affine-expr `>=` `0`
| affine-expr `==` `0`
```
This form is sometimes unreadable and painful to use when writing unittests
for Presburger library and tests in general.
This patch extends the parser to allow affine constraints with affine-expr on
the RHS:
```
affine-constraint ::= affine-expr `>=` `affine-expr`
| affine-expr `==` `affine-expr`
```
The internal storage and printing of IntegerSet is still in the original format.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D128915
We can canonicalize consecutive complex.exp and complex.log which are inverse functions each other.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D128966
This is a followup to D128847. The `AffineMap::getPermutedPosition` method performs a linear scan of the map, thus the previous implementation had asymptotic complexity of `O(|topSort| * |m|)`. This change reduces that to `O(|topSort| + |m|)`.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D129011
The TOSA Specification doesn't have a dilation attribute for transpose_conv2d,
and the padding array is of size 4. (top,bottom,left,right).
This change updates the dialect to match the specification, and updates the lit
tests to match the dialect changes.
Differential Revision: https://reviews.llvm.org/D127332
This also changes the space of the returned lexmin for IntegerPolyhedrons;
the symbols in the poly now correspond to symbols in the result rather than dims.
Reviewed By: Groverkss
Differential Revision: https://reviews.llvm.org/D128933
This allows purging references of scf.ForeachThreadOp and scf.PerformConcurrentlyOp from
ParallelInsertSliceOp.
This will allowmoving the op closer to tensor::InsertSliceOp with which it should share much more
code.
In the future, the decoupling will also allow extending the type of ops that can be used in the
parallel combinator as well as semantics related to multiple concurrent inserts to the same
result.
Differential Revision: https://reviews.llvm.org/D128857
This revision avoids a crash in the 0-D case of distributing vector.transfer ops out of
vector.warp_execute_on_lane_0.
Due to the code complexity and lack of documentation, it took untangling the implementation
before realizing that the simple fix was to fail in the 0-D case.
The rewrite is still very useful to understand this code better.
Differential Revision: https://reviews.llvm.org/D128793
This differential improves two error conditions, by detecting them earlier and by providing better messages to help users understand what went wrong.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D128555
This patch implements the analysis state classes needed for sparse data-flow analysis and implements a dead-code analysis using those states to determine liveness of blocks, control-flow edges, region predecessors, and function callsites.
Depends on D126751
Reviewed By: rriddle, phisiart
Differential Revision: https://reviews.llvm.org/D127064
For the conversion to nvgpu `mma.sync` and `ldmatrix` pathways, the code
was missing support for the `i4` data type. While fixing this, another
bug was discoverd that caused the number of ldmatrix tiles calculated for
certain operand types and configurations to be incorrect. This change
fixes both issues and adds additional tests.
Differential Revision: https://reviews.llvm.org/D128074
This revision merges the 2 split_reduction transforms and adds extra control by using attributes.
SplitReduction is known to require a concrete additional buffer to store tempoaray information.
Add an option to introduce a `bufferization.alloc_tensor` instead of `linalg.init_tensor`.
This behaves better with subset-based tiling and bufferization.
Differential Revision: https://reviews.llvm.org/D128722
When the iteration graph is cyclic (even after several attempts using less and less constraints), the current sparse compiler bails out, and no rewriting hapens. However, this revision adds some new logic where the sparse compiler tries to find a single input sparse tensor that breaks the cycle, and then adds a proper sparse conversion operation. This way, more incoming kernels can be handled!
Note, the resulting code is not optimal (although it keeps more or less proper "sparse" complexity), and more improvements should be added (especially when the kernel directly yields without computation, such as the transpose example). However, handling is better than not handling ;-)
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D128847
This feature is tested by unit test since not many places in the codebase
use SubElementTypeInterface.
Differential Revision: https://reviews.llvm.org/D127539
Since only mutable types and attributes can go into infinite recursion
inside SubElementInterface::walkSubElement, and there are only a few of
them (mutable types and attributes), we introduce new traits for Type
and Attribute: TypeTrait::IsMutable and AttributeTrait::IsMutable,
respectively. They indicate whether a type or attribute is mutable.
Such traits are required if the ImplType defines a `mutate` function.
Then, inside SubElementInterface, we use a set to record visited mutable
types and attributes that have been visited before.
Differential Revision: https://reviews.llvm.org/D127537
We currently generate reproducer configurations using a comment placed at
the top of the generated .mlir file. This is kind of hacky given that comments
have no semantic context in the source file and can easily be dropped. This
strategy also wouldn't work if/when we have a bitcode format. This commit
switches to using an external assembly resource, which is verifiable/can
work with a hypothetical bitcode naturally/and removes the awkward processing
from mlir-opt for splicing comments and re-applying command line options. With
the removal of command line munging, this opens up new possibilities for
executing reproducers in memory.
Differential Revision: https://reviews.llvm.org/D126447
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
The original code is more readable because the goal is to check if the given
value does *not* lie in the range. It is harder to understand this by
reading the rewritten code.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D128753
Should be NFC. We can just do the base conversion manually and avoid
warnings about it. Clang before Clang 13 didn't implement P1825 and
complains:
mlir/lib/Analysis/Presburger/IntegerRelation.cpp:226:10: warning: local variable 'result' will be copied
despite being returned by name [-Wreturn-std-move]
return result;
^~~~~~
mlir/lib/Analysis/Presburger/IntegerRelation.cpp:226:10: note: call 'std::move' explicitly to avoid copying
return result;
^~~~~~
std::move(result)
Consecutive complex.neg are redundant so that we can canonicalize them to the original operands.
Reviewed By: pifon2a
Differential Revision: https://reviews.llvm.org/D128781
1. Remove the redundant collapse clause in MLIR OpenMP worksharing-loop
operation.
2. Fix several typos.
3. Refactor the chunk size type conversion since CreateSExtOrTrunc has
both type check and type conversion.
Reviewed By: kiranchandramohan
Differential Revision: https://reviews.llvm.org/D128338
`enableSplitting` simply enables/disables whether we should split
or use the full buffer. `insertMarkerInOutput` toggles if split markers
should be inserted in between prcessed output chunks.
These options allow for merging the duplicate code paths we have
when splitting is optional.
Differential Revision: https://reviews.llvm.org/D128764
Also added test cases. Also extend support for `computeReprWithOnlyDivLocals` from `IntegerPolyhedron` to `IntegerRelation` and `PresburgerRelation`.
Depends on D128736.
Reviewed By: Groverkss
Differential Revision: https://reviews.llvm.org/D128737
Also added test cases to test this. Both IntegerRelation::addLocalFloorDiv and the fixed implementation of subtraction need to compute division inequalities from dividend and divisor, so this also adds helper util functions to avoid duplicating this logic.
Reviewed By: Groverkss
Differential Revision: https://reviews.llvm.org/D128736
Currently, in the Presburger library, we use the words "variables" and
"identifiers" interchangeably. This patch changes this to only use "variables" to
refer to the variables of PresburgerSpace.
The reasoning behind this change is that the current usage of the word "identifier"
is misleading. variables do not "identify" anything. The information attached to them is the
actual "identifier" for the variable. The word "identifier", will later be used
to refer to the information attached to each variable in space.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D128585
This is already partially the case, but we can rely more heavily on interface libraries and how they are imported/exported in other to simplify the implementation of the mlir python functions in Cmake.
This change also makes a couple of other changes:
1) Add a new CMake function which handles "pure" sources. This was done inline previously
2) Moves the headers associated with CAPI libraries to the libraries themselves. These were previously managed in a separate source target. They can now be added directly to the CAPI libraries using DECLARED_HEADERS.
3) Cleanup some dependencies that showed up as an issue during the refactor
This is a big CMake change that should produce no impact on the build of mlir and on the produced *build tree*. However, this change fixes an issue with the *install tree* of mlir which was previously unusable for projects like torch-mlir because both the "pure" and "extension" targets were pointing to either the build or source trees.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D128230
This pattern can kick in when the source of the broadcast has a shape
that is a prefix/suffix of the result of the shape_cast.
Differential Revision: https://reviews.llvm.org/D128734
Enforce the assumption made on tensor buffers explicitly. When in-place,
reuse the buffer, but fill with all zeroes for the non-update case, since
the kernel assumes all elements are written to. When not in-place, zero
out the new buffer when materializing or when no-updates occur. Copy the
original tensor value when updates occur. This prepares migrating to the
new bufferization strategy, where these assumptions must be made explicit.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D128691
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
This was previous implemented as part of the BufferizableOpInterface of ForEachThreadOp. Moving the implementation to ParallelInsertSliceOp to be consistent with the remaining ops and to have a nice example op that can serve as a blueprint for other ops.
Differential Revision: https://reviews.llvm.org/D128666
Add basic canonicalization for consecutive complex.add and sub operations.
Reviewed By: pifon2a
Differential Revision: https://reviews.llvm.org/D128702
Adding the accumulator value after the `vector.contract` changes the
precision of the operation. This makes sure the accumulator is carried
through to `vector.reduce` (and down to LLVM).
Differential Revision: https://reviews.llvm.org/D128674
This patch adds a `convertFromStorage` field to attribute or type parameters that can implement more complex logic for converting from the parameter's C++ storage type (e.g. `Optional<SmallVector<T>>`) to its C++ type (e.g. `Optional<ArrayRef<T>>`).
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D128293
The parser currently can't parse bare identifiers like 'i0' in affine
maps and sets, and similarly ids like f16/f32. But these bare ids are
part of the grammar - although they are primitive types.
```
error: expected bare identifier
set = affine_set<(i0, i1) : ()>
^
```
This patch allows the parser for AffineMap/IntegerSet to parse bare
identifiers as defined by the grammer.
Reviewed By: bondhugula, rriddle
Differential Revision: https://reviews.llvm.org/D127076
Adding more test cases for sparse_tensor.BinaryOp, including different cases when overlap/left/right region is implemented/empty/identity
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D128383
Previously, the sparse_tensor.unary integration test does not contain cases with the use of `linalg.index` (previoulsy unsupported), this commit adds test cases that use `linalg.index` operators.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D128460
This patch memorize compatible LLVM types in `LLVM::isCompatibleType` in
order to avoid redundant works.
This is especially useful when the size of program is big and there are
multiple occurrences of some deeply nested LLVM struct types, in which
case we can gain quite some speedups with this patch.
Differential Revision: https://reviews.llvm.org/D127918
This patch adds three new LLVM intrinsic operations: llvm.intr.vastart/copy/end.
And its translation from LLVM IR.
This effectively removes a restriction, imposed by 0126dcf1f0, where
non-external functions in LLVM dialect cannot be variadic. At that time
it was not clear how LLVM intrinsics are going to be modeled, which
indirectly affects va_start/copy/end, the core intrinsics used in
variadic functions. But since we have LLVM intrinsics as normal
MLIR operations, it's not a problem anymore.
Differential Revision: https://reviews.llvm.org/D127540
This change updates all remaining bufferization patterns (except for scf.while) and the remaining bufferization infrastructure to infer the memory space whenever possible instead of falling back to "0". (If a default memory space is set in the bufferization options, we still fall back to that value if the memory space could not be inferred.)
Differential Revision: https://reviews.llvm.org/D128423
Add a failure return value and bufferization options argument. This is to keep a subsequent change smaller.
Differential Revision: https://reviews.llvm.org/D128278
These intrinsics will be needed to convert between fixed-length vectors
and scalable vectors.
This operation will be needed for VLS (vector-length specific)
vectorization, when interfacing with vector functions or intrinsics that
take scalable vectors as operands in a context where the length of our
vectors is known or assumed at compile time, but we still want to
generate scalable vector instructions.
Differential Revision: https://reviews.llvm.org/D127100
An optional thread_dim_mapping index array attribute specifies for each
virtual thread dimension, how it remaps 1-1 to a set of concrete processing
element resources (e.g. a CUDA grid dimension or a level of concrete nested
async parallelism). At this time, the specification is backend-dependent and
is not verified by the op, beyond being an index array attribute.
It is the reponsibility of the lowering to interpret the index array in the
context of the concrete target the op is lowered to, or to ignore it when
the specification is ill-formed or unsupported for a particular target.
Differential Revision: https://reviews.llvm.org/D128633
This allows for better type inference during bufferization and is in preparation of supporting memory spaces.
Differential Revision: https://reviews.llvm.org/D128422
This allows for better type inference during bufferization and is in preparation of supporting memory spaces.
Differential Revision: https://reviews.llvm.org/D128581
This allows for better type inference during bufferization and is in preparation of supporting memory spaces.
Differential Revision: https://reviews.llvm.org/D128580
This allows for better type inference during bufferization and is in preparation of supporting memory spaces.
Differential Revision: https://reviews.llvm.org/D128579
This is useful because the result type of an op can sometimes be inferred from its body (e.g., `scf.if`). This will be utilized in subsequent changes.
Also introduces a new `getBufferType` interface method on BufferizableOpInterface. This method is useful for computing a bufferized block argument type with respect to OpOperand types of the parent op.
Differential Revision: https://reviews.llvm.org/D128420
This attribute is currently supported on AllocTensorOp only. Future changes will add support to other ops. Furthermore, the memory space is not propagated properly in all bufferization patterns and some of the core bufferization infrastructure. This will be addressed in a subsequent change.
Differential Revision: https://reviews.llvm.org/D128274
Seems to have been an accident of history and none of these had any reason to be restricted to FuncOp.
Differential Revision: https://reviews.llvm.org/D128614
Mogball