The initial goal of this interface is to fix the current problems with verifying symbol user operations, but can extend beyond that in the future. The current problems with the verification of symbol uses are:
* Extremely inefficient:
Most current symbol users perform the symbol lookup using the slow O(N) string compare methods, which can lead to extremely long verification times in large modules.
* Invalid/break the constraints of verification pass
If the symbol reference is not-flat(and even if it is flat in some cases) a verifier for an operation is not permitted to touch the referenced operation because it may be in the process of being mutated by a different thread within the pass manager.
The new SymbolUserOpInterface exposes a method `verifySymbolUses` that will be invoked from the parent symbol table to allow for verifying the constraints of any referenced symbols. This method is passed a `SymbolTableCollection` to allow for O(1) lookups of any necessary symbol operation.
Differential Revision: https://reviews.llvm.org/D89512
The opposite of tensor_to_memref is tensor_load.
- Add some basic tensor_load/tensor_to_memref folding.
- Add source/target materializations to BufferizeTypeConverter.
- Add an example std bufferization pattern/pass that shows how the
materialiations work together (more std bufferization patterns to come
in subsequent commits).
- In coming commits, I'll document how to write composable
bufferization passes/patterns and update the other in-tree
bufferization passes to match this convention. The populate* functions
will of course continue to be exposed for power users.
The naming on tensor_load/tensor_to_memref and their pretty forms are
not very intuitive. I'm open to any suggestions here. One key
observation is that the memref type must always be the one specified in
the pretty form, since the tensor type can be inferred from the memref
type but not vice-versa.
With this, I've been able to replace all my custom bufferization type
converters in npcomp with BufferizeTypeConverter!
Part of the plan discussed in:
https://llvm.discourse.group/t/what-is-the-strategy-for-tensor-memref-conversion-bufferization/1938/17
Differential Revision: https://reviews.llvm.org/D89437
Parsing of a scalar subview did not create the required static_offsets attribute.
This also adds support for folding scalar subviews away.
Differential Revision: https://reviews.llvm.org/D89467
Added missing strides check to verification method of rank reducing subview
which enforces strides specification for the resulting type.
Differential Revision: https://reviews.llvm.org/D88879
This canonicalization is the counterpart of MemRefCastOp -> LinalgOp but on tensors.
This is needed to properly canonicalize post linalg tiling on tensors.
Differential Revision: https://reviews.llvm.org/D88729
While affine maps are part of the builtin memref type, there is very
limited support for manipulating them in the standard dialect. Add
transpose to the set of ops to complement the existing view/subview ops.
This is a metadata transformation that encodes the transpose into the
strides of a memref.
I'm planning to use this when lowering operations on strided memrefs,
using the transpose to remove the stride without adding a dependency on
linalg dialect.
Differential Revision: https://reviews.llvm.org/D88651
Previously the actual types were not shown, which makes the message
difficult to grok in the context of long lowering chains. Also, it
appears that there were no actual tests for this.
Differential Revision: https://reviews.llvm.org/D88318
This revision introduces a `subtensor` op, which is the counterpart of `subview` for a tensor operand. This also refactors the relevant pieces to allow reusing the `subview` implementation where appropriate.
This operation will be used to implement tiling for Linalg on tensors.
This commit adds support for subviews which enable to reduce resulting rank
by dropping static dimensions of size 1.
Differential Revision: https://reviews.llvm.org/D88534
Adds a pattern that replaces a chain of two tensor_cast operations by a single tensor_cast operation if doing so will not remove constraints on the shapes.
This add canonicalizer for
- extracting an element from a dynamic_tensor_from_elements
- propagating constant operands to the type of dynamic_tensor_from_elements
Differential Revision: https://reviews.llvm.org/D87525
Added support to the Std dialect cast operations to do casts in vector types when feasible.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D87410
This introduces a builder for the more general case that supports zero
elements (where the element type can't be inferred from the ValueRange,
since it might be empty).
Also, fix up some cases in ShapeToStandard lowering that hit this. It
happens very easily when dealing with shapes of 0-D tensors.
The SameOperandsAndResultElementType is redundant with the new
TypesMatchWith and prevented having zero elements.
Differential Revision: https://reviews.llvm.org/D87492
Take advantage of the new `dynamic_tensor_from_elements` operation in `std`.
Instead of stack-allocated memory, we can now lower directly to a single `std`
operation.
Differential Revision: https://reviews.llvm.org/D86935
With `dynamic_tensor_from_elements` tensor values of dynamic size can be
created. The body of the operation essentially maps the index space to tensor
elements.
Declare SCF operations in the `scf` namespace to avoid name clash with the new
`std.yield` operation. Resolve ambiguities between `linalg/shape/std/scf.yield`
operations.
Differential Revision: https://reviews.llvm.org/D86276
Unsigned and Signless attributes use uintN_t and signed attributes use intN_t, where N is the fixed width. The 1-bit variants use bool.
Differential Revision: https://reviews.llvm.org/D86739
Add the unsigned complements to the existing FPToSI and SIToFP operations in the
standard dialect, with one-to-one lowerings to the corresponding LLVM operations.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D85557
This patch moves the registration to a method in the MLIRContext: getOrCreateDialect<ConcreteDialect>()
This method requires dialect to provide a static getDialectNamespace()
and store a TypeID on the Dialect itself, which allows to lazyily
create a dialect when not yet loaded in the context.
As a side effect, it means that duplicated registration of the same
dialect is not an issue anymore.
To limit the boilerplate, TableGen dialect generation is modified to
emit the constructor entirely and invoke separately a "init()" method
that the user implements.
Differential Revision: https://reviews.llvm.org/D85495
- Moved TypeRange into its own header/cpp file, and add hashing support.
- Change FunctionType::get() and TupleType::get() to use TypeRange
Differential Revision: https://reviews.llvm.org/D85075
- Arguments of the first block of a region are considered region arguments.
- Add API on Region class to deal with these arguments directly instead of
using the front() block.
- Changed several instances of existing code that can use this API
- Fixes https://bugs.llvm.org/show_bug.cgi?id=46535
Differential Revision: https://reviews.llvm.org/D83599
The error message in the `std.constant` verifier for function-typed constants
had the name of the undefined function hardcoded to `bar`. Report the actual
name instead.
Differential Revision: https://reviews.llvm.org/D82666
Implement the missing lowering from `std.dim` to the LLVM dialect in case of a
dynamic dimension.
Differential Revision: https://reviews.llvm.org/D81834
Summary:
- Print function name when ReturnOp verification fails
- This helps easily finding the invalid ReturnOp in an IR dump.
Differential Revision: https://reviews.llvm.org/D81513
Summary:
We now support index casting for tensor<index> to tensor<int>. This
better supports compatibility with the Shape dialect.
Differential Revision: https://reviews.llvm.org/D81611
Allow for dynamic indices in the `dim` operation.
Rather than an attribute, the index is now an operand of type `index`.
This allows to apply the operation to dynamically ranked tensors.
The correct lowering of dynamic indices remains to be implemented.
Differential Revision: https://reviews.llvm.org/D81551
This is useful for manipulating the standard dialect from transformations
outside of the standard dialect.
Differential Revision: https://reviews.llvm.org/D80609
