tensor.empty/linalg.init_tensor produces an uninititalized tensor that can be used as a destination operand for destination-style ops (ops that implement `DestinationStyleOpInterface`).
This change makes it possible to implement `TilingInterface` for non-destination-style ops without depending on the Linalg dialect.
RFC: https://discourse.llvm.org/t/rfc-add-tensor-from-shape-operation/65101
Differential Revision: https://reviews.llvm.org/D135129
Summary:
Most of the code that gets `iterator_types` from LinalgInterface is forced to
extract values from an `Attribute`. As a result, the usage pattern looks like
this:
```
SmallVector<StringRef> iterators = llvm::to_vector<4>(linalgOp.iterator_types().getAsValueRange<StringAttr>());
```
It also forces all operations that implement LinalgOp interface to have
`iterator_types` attribute even when the information can be easily infered from
other parameters.
In perfect future, `getIteratorTypeArray` should be the only method to get
iterator types from the interface. The default implementation can rely on
`iterator_types` attribute though.
The name `getIteratorTypeArray` was picked to be consistent with existing
`getIndexingMapsArray`.
This patch add a few sample usages. More cleanups will follow.
Differential Revision: https://reviews.llvm.org/D134729
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.
Reviewed By: mehdi_amini, rriddle
Differential Review: https://reviews.llvm.org/D132838
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.
Reviewed By: mehdi_amini, rriddle
Differential Review: https://reviews.llvm.org/D132838
Previous change (a7bfdc23ab3ade54da99f0f59dababe4d71ae75b) added
support for fusion of `linalg.generic` op with `tensor.expand_shape`
op when the former had multiple results. Fix a bug related to this
that resulted in a segfault.
Reviewed By: hanchung
Differential Revision: https://reviews.llvm.org/D132631
This drops the artificial requirement of producers having a single
result value to be able to fuse with consumers.
The current default also only fuses producer with consumer when the
producer has a single use. This is a simplifying assumption. There are
legitimate use cases where a producer can be fused with consumer and
the fused o pcould be used to replace the uses of the producer as
well. This needs to be done with care to avoid use-def violations. To
allow for downstream users to explore more fusion opportunities, the
core transformation method is exposed as a utility function.
This patch also modifies the control function to take just the fused
operand as the argument. This is enough information for the callers to
get the producer and the consumer operations being considered to
fuse. It also provides information of which producer result is used.
Differential Revision: https://reviews.llvm.org/D132301
This patch removes the `type` field from `Attribute` along with the
`Attribute::getType` accessor.
Going forward, this means that attributes in MLIR will no longer have
types as a first-class concept. This patch lays the groundwork to
incrementally remove or refactor code that relies on generic attributes
being typed. The immediate impact will be on attributes that rely on
`Attribute` containing a type, such as `IntegerAttr`,
`DenseElementsAttr`, and `ml_program::ExternAttr`, which will now need
to define a type parameter on their storage classes. This will save
memory as all other attribute kinds will no longer contain a type.
Moreover, it will not be possible to generically query the type of an
attribute directly. This patch provides an attribute interface
`TypedAttr` that implements only one method, `getType`, which can be
used to generically query the types of attributes that implement the
interface. This interface can be used to retain the concept of a "typed
attribute". The ODS-generated accessor for a `type` parameter
automatically implements this method.
Next steps will be to refactor the assembly formats of certain operations
that rely on `parseAttribute(type)` and `printAttributeWithoutType` to
remove special handling of type elision until `type` can be removed from
the dialect parsing hook entirely; and incrementally remove uses of
`TypedAttr`.
Reviewed By: lattner, rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D130092
While most of methods in ViewLikeInterface accept an `OpFoldResult` for
the offset/size/stride that may be static, represented as `Attribute`,
or dynamic, represented as `Value`, the `Range` abstraction only
accepted `Values`. This can often lead to known-constant
offset/size/strides being materialized into constant operations and
hinder further constant propagation without explicitly running the
constant folding pass. This often leads to a more complicated than
necessary addressing code being emitted. Switch `Range` to use
`OpFoldResult`. Code that uses `Range` currently keeps materializing the
constants to minimize the effect of this change on the IR. Further
commits will make use of this.
Reviewed By: nicolasvasilache, mravishankar
Differential Revision: https://reviews.llvm.org/D129633
This one required more changes than ideal due to overlapping generated name
with different return types. Changed getIndexingMaps to getIndexingMapsArray to
move it out of the way/highlight that it returns (more expensively) a
SmallVector and uses the prefixed name for the Attribute.
Differential Revision: https://reviews.llvm.org/D129919
The rules in the linalg file were very specific to sparse tensors so will
find a better home under sparse tensor dialect than linalg dialect. Also
moved some rewriting from sparsification into this new "pre-rewriting" file.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D129910
It is very wrong if the ranges can't be infered. It's also checked in
verifyStructuredOpInterface, so we don't need the Optional return type.
Reviewed By: springerm
Differential Revision: https://reviews.llvm.org/D124596
This change generalizes the fusion of `tensor.expand_shape` ->
`linalg.generic` op by collapsing to handle cases where only a subset
of the reassociations specified in the `tensor.expand_shape` are valid
to be collapsed.
The method that does the collapsing is refactored to allow it to be a
generic utility when required.
Reviewed By: gysit
Differential Revision: https://reviews.llvm.org/D123153
The method to add elementwise ops fusion patterns pulls in many other
patterns by default. The patterns to pull in along with the
elementwise op fusion should be upto the caller. Split the method to
pull in just the elementwise ops fusion pattern. Other cleanup changes
include
- Move the pattern for constant folding of generic ops (currently only
constant folds transpose) into a separate file, cause it is not
related to fusion
- Drop the uber LinalgElementwiseFusionOptions. With the
populateElementwiseOpsFusionPatterns being split, this has no
utility now.
- Drop defaults for the control function.
- Fusion of splat constants with generic ops doesnt need a control
function. It is always good to do.
Differential Revision: https://reviews.llvm.org/D123236
ExpandShapeOp builder cannot infer the result type since it doesn't know
how the dimension needs to be split. Remove this builder so that it
doesn't get used accidently. Also remove one potential path using it in
generic fusion.
Differential Revision: https://reviews.llvm.org/D122019
Now that sparse tensor types are first-class citizens and the sparse compiler
is taking shape, it is time to make sure other compiler optimizations compose
well with sparse tensors. Mostly, this should be completely transparent (i.e.,
dense and sparse take the same path). However, in some cases, optimizations
only make sense in the context of sparse tensors. This is a first example of
such an optimization, where fusing a sampled elt-wise multiplication only makes
sense when the resulting kernel has a potential lower asymptotic complexity due
to the sparsity.
As an extreme example, running SDDMM with 1024x1024 matrices and a sparse
sampling matrix with only two elements runs in 463.55ms in the unfused
case but just 0.032ms in the fused case, with a speedup of 14485x that
is only possible in the exciting world of sparse computations!
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D120429
It is time to compose Linalg related optimizations with SparseTensor
related optimizations. This is a careful first start by adding some
general Linalg optimizations "upstream" of the sparse compiler in the
full sparse compiler pipeline. Some minor changes were needed to make
those optimizations aware of sparsity.
Note that after this, we will add a sparse specific fusion rule,
just to demonstrate the power of the new composition.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D119971
Fusion of `linalg.generic` with
`tensor.expand_shape/tensor.collapse_shape` currently handles fusion
with reshape by expanding the dimensionality of the `linalg.generic`
operation. This helps fuse elementwise operations better since they
are fused at the highest dimensionality while keeping all indexing
maps involved projected permutations. The intent of these is to push
the reshape to the boundaries of functions.
The presence of named ops (or other ops across which the reshape
cannot be propagated) stops the propagation to the edges of the
function. At this stage, the converse patterns that fold the reshapes
with generic ops by collapsing the dimensions of the generic op can
push the reshape towards edges. In particular it helps the case where
reshapes exist in between named ops and generic ops.
`linalg.named_op` -> `tensor.expand_shape` -> `linalg.generic`
Pushing the reshape down will help fusion of `linalg.named_op` ->
`linalg.generic` using tile + fuse transformations.
This pattern is intended to replace the following patterns
1) FoldReshapeByLinearization : These patterns create indexing maps
that are not projected permutations that affect future
transformations. They are only useful for folding unit-dimensions.
2) PushReshapeByExpansion : This pattern has the same functionality
but has some restrictions
a) It tries to avoid creating new reshapes that limits its
applicability. The pattern added here can achieve the same
functionality through use of the `controlFn` that allows clients
of the pattern freedom to make this decision.
b) It does not work for ops with indexing semantics.
These patterns will be deprecated in a future patch.
Differential Revision: https://reviews.llvm.org/D119365
Reorder the methods and patterns to move related patterns/methods
closer (textually).
Reviewed By: gysit
Differential Revision: https://reviews.llvm.org/D118870
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
Fusion of reshape ops by linearization incorrectly inverted the
indexing map before linearizing dimensions. This leads to incorrect
indexing maps used in the fused operation.
Differential Revision: https://reviews.llvm.org/D117908
This pattern is not written to handle operations with `linalg.index`
operations in its body, i.e. operations that have index semantics.
Differential Revision: https://reviews.llvm.org/D117856
In some cases, fusion can produce illegal operations if after fusion
the range of some of the loops cannot be computed from shapes of its
operands. Check for this case and abort the fusion if this happens.
Differential Revision: https://reviews.llvm.org/D117602
BlockArguments gained the ability to have locations attached a while ago, but they
have always been optional. This goes against the core tenant of MLIR where location
information is a requirement, so this commit updates the API to require locations.
Fixes#53279
Differential Revision: https://reviews.llvm.org/D117633
The input type of a linalg.generic can be less dynamic than its output
type. If this is the case moving a reshape across the generic op would
create invalid IR, as expand_shape cannot expand arbitrary dynamic
dimensions.
Check that the reshape is actually valid before creating the
expand_shape. This exposes the existing verification logic in reshape
utils and removes the incomplete custom implementation in fusion.
Differential Revision: https://reviews.llvm.org/D116600
`tensor.collapse_shape` op when fused with a consumer elementwise
`linalg.generic` operation results in creation of tensor.expand_shape
ops. In purely dynamic cases this can end up with a dynamic dimensions
being expanded to more than one dynamic dimension. This is disallowed
by the semantics of `tensor.expand_shape` operation. (While the
transformation is itself correct, its a gap in the specification of
`tensor.expand_shape` that is the issue). So disallow fusions which
result in such a pattern.
Differential Revision: https://reviews.llvm.org/D116703
After removing the range type, Linalg does not define any type. The revision thus consolidates the LinalgOps.h and LinalgTypes.h into a single Linalg.h header. Additionally, LinalgTypes.cpp is renamed to LinalgDialect.cpp to follow the convention adopted by other dialects such as the tensor dialect.
Depends On D115727
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D115728
The new affine map generated by linearizeCollapsedDims should not drop
dimensions. We need to make sure we create a map with at least as many
dimensions as the source map. This prevents
FoldProducerReshapeOpByLinearization from generating invalid IR.
This solves regression in IREE due to e4e4da86af
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D114838
This reverts commit 9a844c2a9b5c09b4c35d573394a99ab860621581.
