New mode option that allows for either running the default fusion kind that happens today or doing either of producer-consumer or sibling fusion. This will also be helpful to minimize the compile-time of the fusion tests.
Reviewed By: bondhugula, dcaballe
Differential Revision: https://reviews.llvm.org/D110102
Let the calling pass or pattern replace the uses of the original root operation. Internally, the tileAndFuse still replaces uses and updates operands but only of newly created operations.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D110169
This revision adds a
```
FlatAffineValueConstraints(ValueRange ivs, ValueRange lbs, ValueRange ubs)
```
method and use it in hoist padding.
Differential Revision: https://reviews.llvm.org/D110427
This revision extracts padding hoisting in a new file and cleans it up in prevision of future improvements and extensions.
Differential Revision: https://reviews.llvm.org/D110414
This patch adds functionality to FlatAffineConstraints to remove local
variables using equalities. This helps in keeping output representation of
FlatAffineConstraints smaller.
This patch is part of a series of patches aimed at generalizing affine
dependence analysis.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D110056
We currently, incorrectly, assume that a range always has at least
one element when building a contiguous range. This commit adds
a proper empty check to avoid crashing.
Differential Revision: https://reviews.llvm.org/D110457
This patch introduces a generic reduction detection utility that works
across different dialecs. It is mostly a generalization of the reduction
detection algorithm in Affine. The reduction detection logic in Affine,
Linalg and SCFToOpenMP have been replaced with this new generic utility.
The utility takes some basic components of the potential reduction and
returns: 1) the reduced value, and 2) a list with the combiner operations.
The logic to match reductions involving multiple combiner operations disabled
until we can properly test it.
Reviewed By: ftynse, bondhugula, nicolasvasilache, pifon2a
Differential Revision: https://reviews.llvm.org/D110303
This has a few benefits:
* It allows for defining parsers/printer code blocks that
can be shared between operations and attribute/types.
* It removes the weird duplication of generic parser/printer hooks,
which means that newly added hooks only require touching one class.
Differential Revision: https://reviews.llvm.org/D110375
These are among the last operations still defined explicitly in C++. I've
tried to keep this commit as NFC as possible, but these ops
definitely need a non-NFC cleanup at some point.
Differential Revision: https://reviews.llvm.org/D110440
* If the input is a constant splat value, we just
need to reshape it.
* If the input is a general constant with one user,
we can also constant fold it, without bloating
the IR.
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D110439
This commits updates the remaining usages of the ArrayRef<Value> based
matchAndRewrite/rewrite methods in favor of the new OpAdaptor
overload.
Differential Revision: https://reviews.llvm.org/D110360
This has been a TODO for a long time, and it brings about many advantages (namely nice accessors, and less fragile code). The existing overloads that accept ArrayRef are now treated as deprecated and will be removed in a followup (after a small grace period). Most of the upstream MLIR usages have been fixed by this commit, the rest will be handled in a followup.
Differential Revision: https://reviews.llvm.org/D110293
Initially, the padding transformation and the related operation were only used
to guarantee static shapes of subtensors in tiled operations. The
transformation would not insert the padding operation if the shapes were
already static, and the overall code generation would actively remove such
"noop" pads. However, this transformation can be also used to pack data into
smaller tensors and marshall them into faster memory, regardless of the size
mismatches. In context of expert-driven transformation, we should assume that,
if padding is requested, a potentially padded tensor must be always created.
Update the transformation accordingly. To do this, introduce an optional
`packing` attribute to the `pad_tensor` op that serves as an indication that
the padding is an intentional choice (as opposed to side effect of type
normalization) and should be left alone by cleanups.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D110425
Add support for intersecting, subtracting, complementing and checking equality of sets having divisions.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D110138
This canonicalization pattern complements the tensor.cast(pad_tensor) one in
propagating constant type information when possible. It contributes to the
feasibility of pad hoisting.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D110343
* Do not discard static result type information that cannot be inferred from lower/upper padding.
* Add optional argument to `PadTensorOp::inferResultType` for specifying known result dimensions.
Differential Revision: https://reviews.llvm.org/D110380
This is only noticeable when using an attribute across dialects I think.
Previously the namespace would be ommited, but it wouldn't matter as
long as the generated code stays within a single namespace.
Differential Revision: https://reviews.llvm.org/D110367
This test makes sure kernels map to efficient sparse code, i.e. all
compressed for-loops, no co-iterating while loops. In addition, this
revision removes the special constant folding inside the sparse
compiler in favor of Mahesh' new generic linalg folding. Thanks!
NOTE: relies on Mahesh fix, which needs to be rebased first
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D110001
DialectAsmParser has a `parseAttribute` member that takes a
contextual type, but DialectAsmPrinter doesn't have the corresponding
member to take advantage of it. As such, custom attribute
implementations can't really use it. This adds the obvious missing
method which fills this hole.
Differential Revision: https://reviews.llvm.org/D110211
Add a PointerProxy similar to the existing iterator_facade_base::ReferenceProxy and return it from the arrow operator. This prevents iterator facades with a reference type that is not a true reference to take the address of a temporary.
Forward the reference type of the mapped_iterator to the iterator adaptor which in turn forwards it to the iterator facade. This fixes mlir::op_iterator::operator->() to take the address of a temporary.
Make some polishing changes to op_iterator and op_filter_iterator.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D109490
Add a helper method to check if an index vector contains a permutation of its indices. Additionally, refactor applyPermutationToVector to take int64_t.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D110135
This pass transforms SCF.ForOp operations to SCF.WhileOp. The For loop condition is placed in the 'before' region of the while operation, and indctuion variable incrementation + the loop body in the 'after' region. The loop carried values of the while op are the induction variable (IV) of the for-loop + any iter_args specified for the for-loop.
Any 'yield' ops in the for-loop are rewritten to additionally yield the (incremented) induction variable.
This transformation is useful for passes where we want to consider structured control flow solely on the basis of a loop body and the computation of a loop condition. As an example, when doing high-level synthesis in CIRCT, the incrementation of an IV in a for-loop is "just another part" of a circuit datapath, and what we really care about is the distinction between our datapath and our control logic (the condition variable).
Differential Revision: https://reviews.llvm.org/D108454
This patch adds mergeLocalIds andmergeSymbolIds as public functions
for FlatAffineConstraints and FlatAffineValueConstraints respectively.
mergeLocalIds is also required to support divisions in intersection,
subtraction, equality checks, and complement for PresburgerSet.
This patch is part of a series of patches aimed at generalizing affine
dependence analysis.
Reviewed By: bondhugula
Differential Revision: https://reviews.llvm.org/D110045
Lots of custom ops have hand-rolled comma-delimited parsing loops, as does
the MLIR parser itself. Provides a standard interface for doing this that
is less error prone and less boilerplate.
While here, extend Delimiter to support <> and {} delimited sequences as
well (I have a use for <> in CIRCT specifically).
Differential Revision: https://reviews.llvm.org/D110122
This revision refactors ElementsAttr into an Attribute Interface.
This enables a common interface with which to interact with
element attributes, without needing to modify the builtin
dialect. It also removes a majority (if not all?) of the need for
the current OpaqueElementsAttr, which was originally intended as
a way to opaquely represent data that was not representable by
the other builtin constructs.
The new ElementsAttr interface not only allows for users to
natively represent their data in the way that best suits them,
it also allows for efficient opaque access and iteration of the
underlying data. Attributes using the ElementsAttr interface
can directly expose support for interacting with the held
elements using any C++ data type they claim to support. For
example, DenseIntOrFpElementsAttr supports iteration using
various native C++ integer/float data types, as well as
APInt/APFloat, and more. ElementsAttr instances that refer to
DenseIntOrFpElementsAttr can use all of these data types for
iteration:
```c++
DenseIntOrFpElementsAttr intElementsAttr = ...;
ElementsAttr attr = intElementsAttr;
for (uint64_t value : attr.getValues<uint64_t>())
...;
for (APInt value : attr.getValues<APInt>())
...;
for (IntegerAttr value : attr.getValues<IntegerAttr>())
...;
```
ElementsAttr also supports failable range/iterator access,
allowing for selective code paths depending on data type
support:
```c++
ElementsAttr attr = ...;
if (auto range = attr.tryGetValues<uint64_t>()) {
for (uint64_t value : *range)
...;
}
```
Differential Revision: https://reviews.llvm.org/D109190
Currently DenseElementsAttr only exposes the ability to get the full range of values for a given type T, but there are many situations where we just want the beginning/end iterator. This revision adds proper value_begin/value_end methods for all of the supported T types, and also cleans up a bit of the interface.
Differential Revision: https://reviews.llvm.org/D104173
SparseElementsAttr currently does not perform any verfication on construction, with the only verification existing within the parser. This revision moves the parser verification to SparseElementsAttr, and also adds additional verification for when a sparse index is not valid.
Differential Revision: https://reviews.llvm.org/D109189
Some patterns may share the common DAG structures. Generate a static
function to do the match logic to reduce the binary size.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D105797
For `memref.subview` operations, when there are more than one
unit-dimensions, the strides need to be used to figure out which of
the unit-dims are actually dropped.
Differential Revision: https://reviews.llvm.org/D109418
Add an interface that allows grouping together all covolution and
pooling ops within Linalg named ops. The interface currently
- the indexing map used for input/image access is valid
- the filter and output are accessed using projected permutations
- that all loops are charecterizable as one iterating over
- batch dimension,
- output image dimensions,
- filter convolved dimensions,
- output channel dimensions,
- input channel dimensions,
- depth multiplier (for depthwise convolutions)
Differential Revision: https://reviews.llvm.org/D109793
This pass transforms SCF.ForOp operations to SCF.WhileOp. The For loop condition is placed in the 'before' region of the while operation, and indctuion variable incrementation + the loop body in the 'after' region. The loop carried values of the while op are the induction variable (IV) of the for-loop + any iter_args specified for the for-loop.
Any 'yield' ops in the for-loop are rewritten to additionally yield the (incremented) induction variable.
This transformation is useful for passes where we want to consider structured control flow solely on the basis of a loop body and the computation of a loop condition. As an example, when doing high-level synthesis in CIRCT, the incrementation of an IV in a for-loop is "just another part" of a circuit datapath, and what we really care about is the distinction between our datapath and our control logic (the condition variable).
Differential Revision: https://reviews.llvm.org/D108454
Add a new version of fusion on tensors that supports the following scenarios:
- support input and output operand fusion
- fuse a producer result passed in via tile loop iteration arguments (update the tile loop iteration arguments)
- supports only linalg operations on tensors
- supports only scf::for
- cannot add an output to the tile loop nest
The LinalgTileAndFuseOnTensors pass tiles the root operation and fuses its producers.
Reviewed By: nicolasvasilache, mravishankar
Differential Revision: https://reviews.llvm.org/D109766
The discussion on forum:
https://llvm.discourse.group/t/bug-in-partial-dialect-conversion/4115
The `applyPartialConversion` didn't handle the operations, that were
marked as illegal inside dynamic legality callback.
Instead of reporting error, if such operation was not converted to legal set,
the method just added it to `unconvertedSet` in the same way as unknown operations.
This patch fixes that and handle dynamically illegal operations as well.
The patch includes 2 fixes for existing passes:
* `tensor-bufferize` - explicitly mark `std.return` as legal.
* `convert-parallel-loops-to-gpu` - ugly fix with marking visited operations
to avoid recursive legality checks.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D108505
Add a constant propagator for gpu.launch op in cases where the
grid/thread IDs can be trivially determined to take a single constant
value of zero.
Differential Revision: https://reviews.llvm.org/D109994
Note that this revision adds a very tiny bit of constant folding in the
sparse compiler lattice construction. Although I am generally trying to
avoid such canonicalizations (and rely on other passes to fix this instead),
the benefits of avoiding a very expensive disjunction lattice construction
justify having this special code (at least for now).
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D109939
Add the addTileLoopIvsToIndexOpResults method to shift the IndexOp results after tiling.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D109761
Express the input shape definitions of convolution and pooling operations in terms of the output shapes, filter shapes, strides, and dilations.
Reviewed By: shabalin, rsuderman, stellaraccident
Differential Revision: https://reviews.llvm.org/D109815
Adds a new rewrite directive returnType that can be added at the end of an op's
argument list to explicitly specify return types.
```
(OpX $v0, $v1, (returnType "$_builder.getI32Type()"))
```
Pass in a bound value to copy its return type, or pass a native code call to
dynamically create new types.
```
(OpX $v0, $v1, (returnType $v0, (NativeCodeCall<"..."> $v1)))
```
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D109472
Sumesh Udayakumaran