NFC. Clean up memref utils library. This library had a single function
that was completely misplaced. MemRefUtils is expected to be (also per
its comment) a library providing analysis/transforms utilities on memref
dialect ops or memref types. However, in reality it had a helper that
was depended upon by the MemRef dialect, i.e., it was a helper for the
dialect ops library and couldn't contain anything that itself depends on
the MemRef dialect. Move the single method to the memref dialect that
will now allow actual utilities depending on the memref dialect to be
placed in it.
Put findDealloc in the `memref` namespace. This is a pure move.
Differential Revision: https://reviews.llvm.org/D121273
ValueShapeRange::getShape() returns ShapeAdaptor rather than ShapedType
and ShapeAdaptor allows implicit conversion to bool. It ends up that
ShapedTypeComponents can be constructed with ShapeAdaptor incorrectly.
The reason is that the type trait
std::is_constructible<ShapeStorageT, Arg>::value
is fulfilled because ShapeAdaptor can be converted to bool and it can be
used to construct ShapeStorageT. In the end, we won't give any warning
or error message when doing things like
inferredReturnShapes.emplace_back(valueShapeRange.getShape(0));
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D120845
Currently when we fold an empty loop, we assume that any loop
with iterArgs returns its iterArgs in order, which is not always
the case. It may return values defined outside of the loop or
return its iterArgs out of order. This patch adds support to
those cases.
Reviewed By: dcaballe
Differential Revision: https://reviews.llvm.org/D120776
This revision adds support for the linalg.index to the sparse compiler
pipeline. In essence, this adds the ability to refer to indices in
the tensor index expression, as illustrated below:
Y[i, j, k, l, m] = T[i, j, k, l, m] * i * j
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D121251
BuiltinOps.h
These includes are going to be removed from BuiltinOps.h in a followup
when FuncOp is moved out of the Builtin dialect. This commit
pre-emptively adds those includes to simplify the patch moving FuncOp.
It's fine to use any integer (vector) values regardless of the
signedness. The opcode decides how to interpret the bits.
Reviewed By: hanchung
Differential Revision: https://reviews.llvm.org/D121238
This feels like a layering violation, but it fixes the build.
Fixes#54242
tools/mlir/lib/Dialect/GPU/CMakeFiles/obj.MLIRGPUTransforms.dir/Transforms/SerializeToHsaco.cpp.o:SerializeToHsaco.cpp:function (anonymous namespace)::SerializeToHsacoPass::optimizeLlvm(llvm::Module&, llvm::TargetMachine&):
error: undefined reference to 'mlir::makeOptimizingTransformer(unsigned int, unsigned int, llvm::TargetMachine*)'
This pass doesn't rely on any specific characteristics of FuncOp, and
can just be a generic operation pass.
Differential Revision: https://reviews.llvm.org/D121193
It is currently a module pass, but shouldn't be. All of the patterns
are local conversions, and don't require anything about
functions/modules.
Differential Revision: https://reviews.llvm.org/D121192
These passes generally don't rely on any special aspects of FuncOp, and moving allows
for these passes to be used in many more situations. The passes that obviously weren't
relying on invariants guaranteed by a "function" were updated to be generic pass, the
rest were updated to be FunctionOpinterface InterfacePasses.
The test updates are NFC switching from implicit nesting (-pass -pass2) form to
the -pass-pipeline form (generic passes do not implicitly nest as op-specific passes do).
Differential Revision: https://reviews.llvm.org/D121190
FuncOp isn't really important to hardcode here, it is only used to act
as a root operation for the transformation.
Differential Revision: https://reviews.llvm.org/D121195
A lot of test passes are currently anchored on FuncOp, but this
dependency
is generally just historical. A majority of these test passes can run on
any operation, or can operate on a specific interface
(FunctionOpInterface/SymbolOpInterface).
This allows for greatly reducing the API dependency on FuncOp, which
is slated to be moved out of the Builtin dialect.
Differential Revision: https://reviews.llvm.org/D121191
Commit rG1a2bb03edab9d7aa31beb587d0c863acc6715d27 introduced a pattern
to convert dynamic dimensions in operands of `GenericOp`s to static
values based on indexing maps and shapes of other operands. The logic
is directly usable to any `LinalgOp`. Move that pattern as an
`OpInterfaceRewritePattern`.
Differential Revision: https://reviews.llvm.org/D120968
This is a pass that can be used by downstream consumers directly
to avoid the boilerplate to wrap around the `populate*Patterns`.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D121222
A `tensor.cast` consumer can be folded with its producer. This is
beneficial only if the result of the tensor cast is more static than
the source. This patch adds a utility function to check that this is
the case, and adds a couple of canonicalizations patterns that fold an
operation with `tensor.cast` conusmers.
Reviewed By: gysit
Differential Revision: https://reviews.llvm.org/D120950
It's valid to create a TypedArrayAttr or MixedContainerType with
nullptr, e.g.,
std::vector<mlir::Attribute> attrs = {mlir::StringAttr()};
builder.createArrayAttr(attrs);
The predicate didn't check if it's a nullptr and it ended up a crash in
the attribute static verifier. We always check if an attribute is null
so it's better to align the check for these two container type attr.
Reviewed By: rdzhabarov
Differential Revision: https://reviews.llvm.org/D121178
With the recent improvements to OpDSL it is cheap to reintroduce a linalg.copy operation.
This operation is needed in at least 2 cases:
1. for copies that may want to change the elemental type (e.g. cast, truncate, quantize, etc)
2. to specify new tensors that should bufferize to a copy operation. The linalg.generic form
always folds away which is not always the right call.
Differential Revision: https://reviews.llvm.org/D121230
Allow pointwise operations to take rank zero input tensors similarly to scalar inputs. Use an empty indexing map to broadcast rank zero tensors to the iteration domain of the operation.
Depends On D120734
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120807
The revision removes the SoftPlus2DOp operation that previously served as a test operation. It has been replaced by the elemwise_unary operation, which is now used to test unary log and exp functions.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120794
Simplify tests that use `linalg.fill_rng_2d` to focus on testing the `const` and `index` functions. Additionally, cleanup emit_misc.py to use simpler test functions and fix an error message in config.py.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120734
Extend OpDSL with a `defines` method that can set the `hasCanonicalizer` flag for an OpDSL operation. If the flag is set via `defines(Canonicalizer)` the operation needs to implement the `getCanonicalizationPatterns` method. The revision specifies the flag for linalg.fill_tensor and adds an empty `FillTensorOp::getCanonicalizationPatterns` implementation.
This revision is a preparation step to replace linalg.fill by its OpDSL counterpart linalg.fill_tensor. The two are only functionally equivalent if both specify the same canonicalization patterns. The revision is thus a prerequisite for the linalg.fill replacement.
Depends On D120725
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120726
Enhance `LinalgTileAndFuseTensorOpsPattern` with an additional rewrite signature that returns the result of the rewrite.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D121212
Add a FillOpInterface similar to the contraction and convolution op interfaces. The FillOpInterface is a preparation step to replace linalg.fill by its OpDSL version linalg.fill_tensor. The interface implements the `value()`, `output()`, and `result()` methods that by default are not available on linalg.fill_tensor.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120725
Currently, the transfer mask is materialized by generating the vector
comparison: [offset + 0, .., offset + length - 1] < [dim, .., dim]
A better alternative is to materialize the transfer mask by using the
operation: `vector.create_mask (dim - offset)`, which will generate
simpler code and compose better with scalable vectors.
Differential Revision: https://reviews.llvm.org/D120487
Rewrite isInnermostAffineForOp utility to make it more direct/efficient.
Drop unnecessary check. NFC.
Differential Revision: https://reviews.llvm.org/D121170
This is to align with the PyTACO API better.
Modify an existing unit test to test the new routines.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D121083
This patch cleans up the interface to PresburgerSet. At a high level it does
the following changes:
- Move member functions around to have constructors at top and print/dump
at end.
- Move a private function to be a static function instead.
- Change member functions of type "getAllIntegerPolyhedron" to "getAllPolys"
instead.
- Improve documentation for PresburgerSet.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D121027
In quantized comutation, there are casting ops around computation ops.
Reorder the ops to make reduce-to-contract actually work.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D120760
The current StandardToLLVM conversion patterns only really handle
the Func dialect. The pass itself adds patterns for Arithmetic/CFToLLVM, but
those should be/will be split out in a followup. This commit focuses solely
on being an NFC rename.
Aside from the directory change, the pattern and pass creation API have been renamed:
* populateStdToLLVMFuncOpConversionPattern -> populateFuncToLLVMFuncOpConversionPattern
* populateStdToLLVMConversionPatterns -> populateFuncToLLVMConversionPatterns
* createLowerToLLVMPass -> createConvertFuncToLLVMPass
Differential Revision: https://reviews.llvm.org/D120778
These unit tests resides in an internal repository. Porting the tests to the
public repository.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D121021
The default lowering of vector transpose operations generates a large sequence of
scalar extract/insert operations, one pair for each scalar element in the input tensor.
In other words, the vector transpose is scalarized. However, there are transpose
patterns where one or more adjacent high-order dimensions are not transposed (for
example, in the transpose pattern [1, 0, 2, 3], dimensions 2 and 3 are not transposed).
This patch improves the lowering of those cases by not scalarizing them and extracting/
inserting a full n-D vector, where 'n' is the number of adjacent high-order dimensions
not being transposed. By doing so, we prevent the scalarization of the code and generate a
more performant vector version.
Paradoxically, this patch shouldn't improve the performance of transpose operations if
we are using LLVM. The LLVM pipeline is able to optimize away some of the extract/insert
operations and the SLP vectorizer is converting the scalar operations back to its vector
form. However, scalarizing a vector version of the code in MLIR and relying on the SLP
vectorizer to reconstruct the vector code again is highly undesirable for several reasons.
Reviewed By: nicolasvasilache, ThomasRaoux
Differential Revision: https://reviews.llvm.org/D120601
This patch fixes the crash when printing some ops (like affine.for and
scf.for) when they are dumped in invalid state, e.g. during pattern
application. Now the AsmState constructor verifies the operation
first and switches to generic operation printing when the verification
fails. Also operations are now printed in generic form when emitting
diagnostics and the severity level is Error.
Reviewed By: rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D117834
Uday Bondhugula