They used to be classes with a virtual `run` function. This was inconvenient because post analysis steps are stored in BufferizationOptions. Because of this design choice, BufferizationOptions were not copyable.
Differential Revision: https://reviews.llvm.org/D119258
Supports whitespace elements: ` ` and `\\n` as well as the "empty" whitespace `` that removes an otherwise printed space.
Depends on D118208
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D118210
Reuse the higher precision F32 approximation for the F16 one (by expanding and
truncating). This is partly RFC as I'm not sure what the expectations are here
(e.g., these are only for F32 and should not be expanded, that reusing
higher-precision ones for lower precision is undesirable due to increased
compute cost and only approximations per exact type is preferred, or this is
appropriate [at least as fallback] but we need to see how to make it more
generic across all the patterns here).
Differential Revision: https://reviews.llvm.org/D118968
Implements optional attribute or type parameters, including support for such parameters in the assembly format `struct` directive. Also implements optional groups.
Depends on D117971
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D118208
For 0-D as well as 1-D vectors, both these patterns should
return a failure as there is no need to collapse the shape
of the source. Currently, only 1-D vectors were handled. This
patch handles the 0-D case as well.
Reviewed By: Benoit, ThomasRaoux
Differential Revision: https://reviews.llvm.org/D119202
There are a few different test passes that check elementwise fusion in
Linalg. Consolidate them to a single pass controlled by different pass
options (in keeping with how `TestLinalgTransforms` exists).
Add a Python method, output_sparse_tensor, to use sparse_tensor.out to write
a sparse tensor value to a file.
Modify the method that evaluates a tensor expression to return a pointer of the
MLIR sparse tensor for the result to delay the extraction of the coordinates and
non-zero values.
Implement the Tensor to_file method to evaluate the tensor assignment and write
the result to a file.
Add unit tests. Modify test golden files to reflect the change that TNS outputs
now have a comment line and two meta data lines.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D118956
Currently if an operation wants a C++ implemented parser/printer, it specifies inline
code blocks. This is quite problematic for various reasons, e.g. it requires defining
C++ inside of Tablegen which is discouraged when possible, but mainly because
nearly all usages simply forward to static functions (e.g. `static void parseSomeOp(...)`)
with users devising their own standards for how these are defined.
This commit adds support for a `hasCustomAssemblyFormat` bit field that specifies if
a C++ parser/printer is needed, and when set to 1 declares the parse/print methods for
operations to override. For migration purposes, the existing behavior is untouched. Upstream
usages will be replaced in a followup to keep this patch focused on the new implementation.
Differential Revision: https://reviews.llvm.org/D119054
There are a few different test passes that check elementwise fusion in
Linalg. Consolidate them to a single pass controlled by different pass
options (in keeping with how `TestLinalgTransforms` exists).
Fix the verification function of spirv::ConstantOp to allow nesting
array attributes.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D118939
* Implement `FlatAffineConstraints::getConstantBound(EQ)`.
* Inject a simpler constraint for loops that have at most 1 iteration.
* Taking into account constant EQ bounds of FlatAffineConstraint dims/symbols during canonicalization of the resulting affine map in `canonicalizeMinMaxOp`.
Differential Revision: https://reviews.llvm.org/D119153
This is both more efficient and more ergonomic to use, as inverting a
bit vector is trivial while inverting a set is annoying.
Sadly this leaks into a bunch of APIs downstream, so adapt them as well.
This would be NFC, but there is an ordering dependency in MemRefOps's
computeMemRefRankReductionMask. This is now deterministic, previously it
was dependent on SmallDenseSet's unspecified iteration order.
Differential Revision: https://reviews.llvm.org/D119076
Adapt `tileConsumerAndFuseProducers` to return failure if the generated tile loop nest is empty since all tile sizes are zero. Additionally, fix `LinalgTileAndFuseTensorOpsPattern` to return success if the pattern applied successfully.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D118878
Simple pass that changes all symbols to private unless symbol is excluded (and
in which case there is no change to symbol's visibility).
Differential Revision: https://reviews.llvm.org/D118752
Induction variable calculation was ignoring scf.for step value. Fix it to get
the correct induction variable value in the prologue.
Differential Revision: https://reviews.llvm.org/D118932
Some translations do work with unregistered dialects, this allows one
to write testcases against them. It works the same way as it does for
mlir-opt.
Differential Revision: https://reviews.llvm.org/D118872
Replace the Python implementation for reading tensor input data from files with
create_sparse_tensor that uses sparse_tensor.new.
The MLIR TNS format has two extra meta data lines. Add the extra meta data to a
test data file.
Implement TACO tensor methods evaluate and unpack.
Add unit tests.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D118803
-- This commit adds a canonicalization pattern on scf.while to remove
the loop invariant arguments.
-- An argument is considered loop invariant if the iteration argument value is
the same as the corresponding one being yielded (at the same position) in both
the before/after block of scf.while.
-- For the arguments removed, their use within scf.while and their corresponding
scf.while's result are replaced with their corresponding initial value.
Signed-off-by: Abhishek Varma <abhishek.varma@polymagelabs.com>
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D116923
Prior to this patch, using an operation without any results as the location would result in the generation of invalid C++ code. It'd try to format using the result values, which would would end up being an empty string for an operation without any.
This patch fixes that issue by instead using getValueAndRangeUse which handles both ranges as well as the case for an op without any results.
Differential Revision: https://reviews.llvm.org/D118885
The code assumes that a TypeConstraint in the additional constraints list specifies precisely one argument.
If the user were to not specify any, it'd result in a crash. If given more than one, the additional ones were ignored.
This patch fixes the crash and disallows user errors by adding a check that a single argument is supplied to the TypeConstraint
Differential Revision: https://reviews.llvm.org/D118763
This is completely unused upstream, and does not really have well defined semantics
on what this is supposed to do/how this fits into the ecosystem. Given that, as part of
splitting up the standard dialect it's best to just remove this behavior, instead of try
to awkwardly fit it somewhere upstream. Downstream users are encouraged to
define their own operations that clearly can define the semantics of this.
This also uncovered several lingering uses of ConstantOp that weren't
updated to use arith::ConstantOp, and worked during conversions because
the constant was removed/converted into something else before
verification.
See https://llvm.discourse.group/t/standard-dialect-the-final-chapter/ for more discussion.
Differential Revision: https://reviews.llvm.org/D118654
This is part of the larger effort to split the standard dialect. This will also allow for pruning some
additional dependencies on Standard (done in a followup).
Differential Revision: https://reviews.llvm.org/D118202
Currently if an operation requires additional verification, it specifies an inline
code block (`let verifier = "blah"`). This is quite problematic for various reasons, e.g.
it requires defining C++ inside of Tablegen which is discouraged when possible, but mainly because
nearly all usages simply forward to a static function `static LogicalResult verify(SomeOp op)`.
This commit adds support for a `hasVerifier` bit field that specifies if an additional verifier
is needed, and when set to `1` declares a `LogicalResult verify()` method for operations to
override. For migration purposes, the existing behavior is untouched. Upstream usages will
be replaced in a followup to keep this patch focused on the hasVerifier implementation.
One main user facing change is that what was one `MyOp::verify` is now `MyOp::verifyInvariants`.
This better matches the name this method is called everywhere else, and also frees up `verify` for
the user defined additional verification. The `verify` function when generated now (for additional
verification) is private to the operation class, which should also help avoid accidental usages after
this switch.
Differential Revision: https://reviews.llvm.org/D118742
This CL supports adding dependency between traits verifiers and the
dependency will be checked statically.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D115135
This revision avoids incorrect hoisting of alloca'd buffers across an AutomaticAllocationScope boundary.
In the more general case, we will probably need a ParallelScope-like interface.
Differential Revision: https://reviews.llvm.org/D118768
By fully qualifying the use of any types and functions from the mlir namespace, users are not required to add using namespace mlir; into the C++ file including the Tablegen output.
Differential Revision: https://reviews.llvm.org/D118767
Use type inference when building the TransferWriteOp in the TransferWritePermutationLowering. Previously, the result type has been set to Type() which triggers an assertion if the pattern is used with tensors instead of memrefs.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D118758
Move the functions that retrieve the supporting C library, compile an MLIR
module and build a JIT execution engine to mlir_pytaco_utils.
Add a function to create an MLIR sparse tensor from a file and return a pointer
to the MLIR sparse tensor as well as the shape of the sparse tensor.
Add unit tests.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D118496
Exposes mlir::DialectRegistry to the C API as MlirDialectRegistry along with
helper functions. A hook has been added to MlirDialectHandle that inserts
the dialect into a registry.
A future possible change is removing mlirDialectHandleRegisterDialect in
favor of using mlirDialectHandleInsertDialect, which it is now implemented with.
Differential Revision: https://reviews.llvm.org/D118293
When attempting to cast a pybind11 handle to an MLIR C API object through
capsules, the binding code would attempt to directly access the "_CAPIPtr"
attribute on the object, leading to a rather obscure AttributeError when the
attribute was missing, e.g., on non-MLIR types. Check for its presence and
throw a TypeError instead.
Depends On D117646
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D117658
Somehow the test introduced in https://reviews.llvm.org/D118006 produces the expected result but running
through lli with Intel SDE activated sneaks in an error code 2 (before this commit) or an error code 10
(after this commit).
The test as is is still meaningful in that the LLVMIR generation would crash if the `elementtype` is set
improperly.
Still, this should run with lli turned on.
Matthias Springer