For the getters, it is bad practice to keep the reference
around for too long, as explained in the new comment
Reviewed By: gussmith23
Differential Revision: https://reviews.llvm.org/D105599
This patch adds full qualification to the types and function calls used inside of (Static)InterfaceMethod in OpInterfaces. Without this patch using many of these interfaces in a downstream project yields compiler errors as the types and default implementations are mostly copied verbatim. Without then putting using namespace mlir; in the header file of the implementations of those interfaces, compilation is impossible.
Using fully qualified lookup fixes this issue.
Differential Revision: https://reviews.llvm.org/D105619
This reverts commit 6c0fd4db79.
This simple implementation is unfortunately not extensible and needs to be reverted.
The extensible way should be to extend https://reviews.llvm.org/D104321.
Introduce the exp and log function in OpDSL. Add the soft plus operator to test the emitted IR in Python and C++.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D105420
Remove the GenericOpBase class formerly used to factor out common logic shared be GenericOp and IndexedGenericOp. After removing IndexedGenericOp, the base class is not used anymore.
Differential Revision: https://reviews.llvm.org/D105307
This class and classes that extend it are general utilities for any dialect
that is being converted into the LLVM dialect. They are in no way specific to
Standard-to-LLVM conversion and should not make their users depend on it.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D105542
Verify the number of results matches exactly the number of output tensors. Simplify the FillOp verification since part of it got redundant.
Differential Revision: https://reviews.llvm.org/D105427
Simplify vector unrolling pattern to be more aligned with rest of the
patterns and be closer to vector distribution.
The new implementation uses ExtractStridedSlice/InsertStridedSlice
instead of the Tuple ops. After this change the ops based on Tuple don't
have any more used so they can be removed.
This allows removing signifcant amount of dead code and will allow
extending the unrolling code going forward.
Differential Revision: https://reviews.llvm.org/D105381
Add the rewrite of PadTensorOp to InitTensor + InsertSlice before the
bufferization analysis starts.
This is exercised via a more advanced integration test.
Since the new behavior triggers folding, 2 tests need to be updated.
One of those seems to exhibit a folding issue with `switch` and is modified.
Differential Revision: https://reviews.llvm.org/D105549
Refactor the original code to rewrite a PadTensorOp into a
sequence of InitTensorOp, FillOp and InsertSliceOp without
vectorization by default. `GenericPadTensorOpVectorizationPattern`
provides a customized OptimizeCopyFn to vectorize the
copying step.
Reviewed By: silvas, nicolasvasilache, springerm
Differential Revision: https://reviews.llvm.org/D105293
The `bufferizesToMemoryRead` condition was too optimistics in the case
of operands that map to a block argument.
This is the case for ForOp and TiledLoopOp.
For such ops, forward the call to all uses of the matching BBArg.
Differential Revision: https://reviews.llvm.org/D105540
"Standard-to-LLVM" conversion is one of the oldest passes in existence. It has
become quite large due to the size of the Standard dialect itself, which is
being split into multiple smaller dialects. Furthermore, several conversion
features are useful for any dialect that is being converted to the LLVM
dialect, which, without this refactoring, creates a dependency from those
conversions to the "standard-to-llvm" one.
Put several of the reusable utilities from this conversion to a separate
library, namely:
- type converter from builtin to LLVM dialect types;
- utility for building and accessing values of LLVM structure type;
- utility for building and accessing values that represent memref in the LLVM
dialect;
- lowering options applicable everywhere.
Additionally, remove the type wrapping/unwrapping notion from the type
converter that is no longer relevant since LLVM types has been reimplemented as
first-class MLIR types.
Reviewed By: pifon2a
Differential Revision: https://reviews.llvm.org/D105534
When an affine.if operation is returning/yielding results and has a
trivially true or false condition, then its 'then' or 'else' block,
respectively, is promoted to the affine.if's parent block and then, the
affine.if operation is replaced by the correct results/yield values.
Relevant test cases are also added.
Signed-off-by: Srishti Srivastava <srishti.srivastava@polymagelabs.com>
Differential Revision: https://reviews.llvm.org/D105418
Split out GPU ops library from GPU transforms. This allows libraries to
depend on GPU Ops without needing/building its transforms.
Differential Revision: https://reviews.llvm.org/D105472
The normalizeAffineForOp and normalizedAffineParallel methods were
misplaced in the AffineLoopNormalize pass file while their declarations
were in affine utils. Move these to affine Utils.cpp. NFC.
Differential Revision: https://reviews.llvm.org/D105468
Fix dialect conversion ConversionPatternRewriter::cancelRootUpdate: the
erasure of operations here from the list of root update was off by one.
Should have been:
```
rootUpdates.erase(rootUpdates.begin() + (rootUpdates.rend() - it - 1));
```
instead of
```
rootUpdates.erase(rootUpdates.begin() + (rootUpdates.rend() - it));
```
or more directly:
```
rootUpdates.erase(it.base() - 1)
```
While on this, add an assertion to improve dev experience when a cancel is
called on an op on which a root update hasn't been started.
Differential Revision: https://reviews.llvm.org/D105397
Fix FlatAffineConstraints::getConstantBoundOnDimSize to ensure that
returned bounds on dim size are always non-negative regardless of the
constraints on that dimension. Add an assertion at the user.
Differential Revision: https://reviews.llvm.org/D105171
Unbreaks building mlir-reduce when `DLLVM_INCLUDE_TESTS` is set to OFF.
The dependency MLIRTestDialect is only available if building with tests.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D105434
This changes the custom syntax of the emitc.include operation for standard includes.
Reviewed By: marbre
Differential Revision: https://reviews.llvm.org/D105281
Opaque attributes that currently contain string literals can't currently be properly roundtripped as they are not printed as escaped strings. This leads to incorrect tokens being generated and the parser to almost certainly fail. This patch simply uses llvm::printEscapedString from LLVM. It escapes all non printable characters and quotes to \xx hex literals, and backslashes to two backslashes. This syntax is supported by MLIRs Lexer as well. The same function is also currently in use for the same purpose in printSymbolReference, printAttribute for StringAttr and many more in AsmPrinter.cpp.
Differential Revision: https://reviews.llvm.org/D105405
Different constraints may share the same predicate, in this case, we
will generate duplicate ODS verification function.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D104369
Remove `getDynOperands` and `createOrFoldDimOp` from MemRef.h to decouple MemRef a bit from Tensor. These two functions are used in other dialects/transforms.
Differential Revision: https://reviews.llvm.org/D105260
Basically every kind of parseOptional* method in DialectAsmParser has a corresponding parse* method which will emit an error if the requested token has not been found. An odd one out of this rule is parseOptionalString which does not have a corresponding parseString method.
This patch adds that method and implements it in basically the same fashion as parseKeyword, by first going through parseOptionalString and emitting an error on failure.
Differential Revision: https://reviews.llvm.org/D105406
Same as other CreateLoad-style APIs, these need an explicit type
argument to support opaque pointers.
Differential Revision: https://reviews.llvm.org/D105395
This revision extends the sparse compiler support from fp/int addition and multiplication to fp/int negation and subtraction, thereby increasing the scope of sparse kernels that can be compiled.
Reviewed By: gussmith23
Differential Revision: https://reviews.llvm.org/D105306
The implementation has become too unwieldy and cognitive overhead wins.
Instead compress the implementation in preparation for additional lowering paths.
This is a resubmit of https://reviews.llvm.org/D105359 without ordering ambiguities.
Differential Revision: https://reviews.llvm.org/D105367
Fusion by linearization should not happen when
- The reshape is expanding and it is a consumer
- The reshape is collapsing and is a producer.
The bug introduced in this logic by some recent refactoring resulted
in a crash.
To enforce this (negetive) use case, add a test that reproduces the
error and verifies the fix.
Differential Revision: https://reviews.llvm.org/D104970
The implementation has become too unwieldy and cognitive overhead wins.
Instead compress the implementation in preparation for additional lowering paths.
Differential Revision: https://reviews.llvm.org/D105359
Add the min operation to OpDSL and introduce a min pooling operation to test the implementation. The patch is a sibling of the max operation patch https://reviews.llvm.org/D105203 and the min operation is again lowered to a compare and select pair.
Differential Revision: https://reviews.llvm.org/D105345
To make TensorExp clearer, this change refactors the e0/e1 fields into a union: e0/e1 for a binary op tensor expression, and tensor_num for a tensor-kinded tensor expression.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D105303
Introduce an integration test folder in the test/python subfolder and move the opsrun.py test into the newly created folder. The test verifies named operations end-to-end using both the yaml and the python path.
Differential Revision: https://reviews.llvm.org/D105276
Add the max operation to the OpDSL and introduce a max pooling operation to test the implementation. As MLIR has no builtin max operation, the max function is lowered to a compare and select pair.
Differential Revision: https://reviews.llvm.org/D105203
Nicolas Vasilache