Currently, vector.contract joins the intermediate result and the accumulator
argument (of ranks K) using summation. We desire more joining operations ---
such as max --- to help vector.contract express reductions. This change extends
Vector_ContractionOp to take an optional attribute (called "kind", of enum type
CombiningKind) specifying the joining operation to be add/mul/min/max for int/fp
, and and/or/xor for int only. By default this attribute has value "add".
To implement this we also need to extend vector.outerproduct, since
vector.contract gets transformed to vector.outerproduct (and that to
vector.fma). The extension for vector.outerproduct is also an optional kind
attribute that uses the same enum type and possible values. The default is
"add". In case of max/min we transform vector.outerproduct to a combination of
compare and select.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D93280
These patterns move vector.bitcast ops to be before
insert ops or after extract ops where suitable.
With them, bitcast will happen on smaller vectors
and there are more chances to share extract/insert
ops.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D96040
This patch adds patterns to use vector.shape_cast to cast
away leading 1-dimensions from a few vector operations.
It allows exposing more canonical forms of vector.transfer_read,
vector.transfer_write, vector_extract_strided_slice, and
vector.insert_strided_slice. With this, we can have more
opportunity to cancelling extract/insert ops or forwarding
write/read ops.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D95873
In the overwhelmingly common case, enum attribute case strings represent valid identifiers in MLIR syntax. This revision updates the format generator to format as a keyword in these cases, removing the need to wrap values in a string. The parser still retains the ability to parse the string form, but the printer will use the keyword form when applicable.
Differential Revision: https://reviews.llvm.org/D94575
Adding the ability to index the base address brings these operations closer
to the transfer read and write semantics (with lowering advantages), ensures
more consistent use in vector MLIR code (easier to read), and reduces the
amount of code duplication to lower memrefs into base addresses considerably
(making codegen less error-prone).
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D94278
Extend unroll to support all element-wise ops and allow unrolling for ops with
vector operands of with the same shape as the destination but different element
type (like Cmp or Select).
Differential Revision: https://reviews.llvm.org/D93121
This revision refactors the way that attributes/types are considered when generating aliases. Instead of considering all of the attributes/types of every operation, we perform a "fake" print step that prints the operations using a dummy printer to collect the attributes and types that would actually be printed during the real process. This removes a lot of attributes/types from consideration that generally won't end up in the final output, e.g. affine map attributes in an `affine.apply`/`affine.for`.
This resolves a long standing TODO w.r.t aliases, and helps to have a much cleaner textual output format. As a datapoint to the latter, as part of this change several tests were identified as testing for the presence of attributes aliases that weren't actually referenced by the custom form of any operation.
To ensure that this wouldn't cause a large degradation in compile time due to the second full print, I benchmarked this change on a very large module with a lot of operations(The file is ~673M/~4.7 million lines long). This file before this change take ~6.9 seconds to print in the custom form, and ~7 seconds after this change. In the custom assembly case, this added an average of a little over ~100 miliseconds to the compile time. This increase was due to the way that argument attributes on functions are structured and how they get printed; i.e. with a better representation the negative impact here can be greatly decreased. When printing in the generic form, this revision had no observable impact on the compile time. This benchmarking leads me to believe that the impact of this change on compile time w.r.t printing is closely related to `print` methods that perform a lot of additional/complex processing outside of the OpAsmPrinter.
Differential Revision: https://reviews.llvm.org/D90512
This revision folds vector.transfer operations by updating the `masked` bool array attribute when more unmasked dimensions can be discovered.
Differential revision: https://reviews.llvm.org/D83586
The UnrollVectorPattern is can be used in a programmable fashion by:
```
OwningRewritePatternList patterns;
patterns.insert<UnrollVectorPattern<AddFOp>>(ArrayRef<int64_t>{2, 2}, ctx);
patterns.insert<UnrollVectorPattern<vector::ContractionOp>>(
ArrayRef<int64_t>{2, 2, 2}, ctx);
...
applyPatternsAndFoldGreedily(getFunction(), patterns);
```
Differential revision: https://reviews.llvm.org/D83064
Summary:
This revision makes the use of vector transfer operatons more idiomatic by
allowing to omit and inferring the permutation_map.
Differential Revision: https://reviews.llvm.org/D80092
Summary:
Update ShapeCastOp folder to use producer-consumer value forwarding.
Support is added for tracking sub-vectors through trivial shape cast operations,
where the sub-vector shape is preserved across shape cast operations and only
leading ones are added or removed.
Support is preserved for cancelling shape cast operations.
One unit test is added and two are updated.
Reviewers: aartbik, nicolasvasilache
Reviewed By: aartbik, nicolasvasilache
Subscribers: frgossen, mehdi_amini, rriddle, jpienaar, burmako, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, liufengdb, Joonsoo, grosul1, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D77253
Summary:
Add support for TupleGetOp folding through InsertSlicesOp and ExtractSlicesOp.
Vector-to-vector transformations for unrolling and lowering to hardware vectors
can generate chains of structured vector operations (InsertSlicesOp,
ExtractSlicesOp and ShapeCastOp) between the producer of a hardware vector
value and its consumer. Because InsertSlicesOp, ExtractSlicesOp and ShapeCastOp
are structured, we can track the location (tuple index and vector offsets) of
the consumer vector value through the chain of structured operations to the
producer, enabling a much more powerful producer-consumer fowarding of values
through structured ops and tuple, which in turn enables a more powerful
TupleGetOp folding transformation.
Reviewers: nicolasvasilache, aartbik
Reviewed By: aartbik
Subscribers: grosul1, mehdi_amini, rriddle, jpienaar, burmako, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, liufengdb, Joonsoo, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D76889
