getMixedOffsets() calls getMixedValues() with `static_offsets` and
`offsets`. It is assumed that the number of dynamic offsets in
`static_offsets` equals the rank of `offsets`. Otherwise, we fail on
assert when trying to access an array out of its bounds.
The same applies to getMixedStrides() and getMixedOffsets().
A verification of this assumption is added to
verifyOffsetSizeAndStrideOp() and a clear assert is added in
getMixedValues().
The motivation is to avoid having to negate `isDynamic*` checks, avoid
double negations, and allow for `ShapedType::isStaticDim` to be used in
ADT functions without having to wrap it in a lambda performing the
negation.
Also add the new functions to C and Python bindings.
Since #130487, `tensor.extract_slice` and `tensor.insert_slice` ops that
are statically detected to go out of bounds are rejected by the
verifier.
This commit fixes canonicalization patterns that currently fold
dynamically out-of-bounds ops (valid IR) to statically out-of-bounds ops
(invalid IR).
This patch is a first pass at making consistent syntax across the
`LinalgTransformOp`s that use dynamic index lists for size parameters.
Previously, there were two different forms: inline types in the list, or
place them in the functional style tuple. This patch goes for the
latter.
In order to do this, the `printPackedOrDynamicIndexList`,
`printDynamicIndexList` and their `parse` counterparts were modified so
that the types can be optionally provided to the corresponding custom
directives.
All affected ops now use tablegen `assemblyFormat`, so custom
`parse`/`print` functions have been removed. There are a couple ops that
will likely add dynamic size support, and once that happens it should be
made sure that the assembly remains consistent with the changes in this
patch.
The affected ops are as follows: `pack`, `pack_greedily`,
`tile_using_forall`. The `tile_using_for` and `vectorize` ops already
used this syntax, but their custom assembly was removed.
---------
Co-authored-by: Oleksandr "Alex" Zinenko <ftynse@gmail.com>
In #71153, the `memref.subview` canonicalizer crashes due to a negative
`size` being passed as an operand. During `SubViewOp::verify` this
negative `size` is not yet detectable since it is dynamic and only
available after constant folding, which happens during the
canonicalization passes. As discussed in
<https://discourse.llvm.org/t/rfc-more-opfoldresult-and-mixed-indices-in-ops-that-deal-with-shaped-values/72510>,
the verifier should not be extended as it should "only verify local
aspects of an operation".
This patch fixes#71153 by not folding in aforementioned situation.
Also, this patch adds a basic offset and size check in the
`OffsetSizeAndStrideOpInterface` verifier.
Note: only `offset` and `size` are checked because `stride` is allowed
to be negative
(54d81e49e3).
Also make `getNumDynamicEntriesUpToIdx` a helper function. It does not have to be an interface method.
Differential Revision: https://reviews.llvm.org/D156864
This change lifts the limitation that only the trailing dimensions/sizes
in dynamic index lists can be scalable. It allows us to extend
`MaskedVectorizeOp` and `TileOp` from the Transform dialect so that the
following is allowed:
%1, %loops:3 = transform.structured.tile %0 [4, [4], [4]]
This is also a follow up for https://reviews.llvm.org/D153372
that will enable the following (middle vector dimension is scalable):
transform.structured.masked_vectorize %0 vector_sizes [2, [4], 8]
To facilate this change, the hooks for parsing and printing dynamic
index lists are updated accordingly (`printDynamicIndexList` and
`parseDynamicIndexList`, respectively). `MaskedVectorizeOp` and `TileOp`
are updated to include an array of attribute of bools that captures
whether the corresponding vector dimension/tile size, respectively, are
scalable or not.
NOTE 1: I am re-landing this after the initial version was reverted. To
fix the regression and in addition to the original patch, this revision
updates the Python bindings for the transform dialect
NOTE 2: This change is a part of a larger effort to enable scalable
vectorisation in Linalg. See this RFC for more context:
* https://discourse.llvm.org/t/rfc-scalable-vectorisation-in-linalg/
This relands 048764f23a380fd6f8cc562a0008dcc6095fb594 with fixes.
Differential Revision: https://reviews.llvm.org/D154336
This change lifts the limitation that only the trailing dimensions/sizes
in dynamic index lists can be scalable. It allows us to extend
`MaskedVectorizeOp` and `TileOp` from the Transform dialect so that the
following is allowed:
%1, %loops:3 = transform.structured.tile %0 [[4], [4], 4]
This is also a follow up for https://reviews.llvm.org/D153372
that will enable the following (middle vector dimension is scalable):
transform.structured.masked_vectorize %0 vector_sizes [2, [4], 8]
To facilate this change, the hooks for parsing and printing dynamic
index lists are updated accordingly (`printDynamicIndexList` and
`parseDynamicIndexList`, respectively). `MaskedVectorizeOp` and `TileOp`
are updated to include an array of attribute of bools that captures
whether the corresponding vector dimension/tile size, respectively, are
scalable or not.
This change is a part of a larger effort to enable scalable
vectorisation in Linalg. See this RFC for more context:
* https://discourse.llvm.org/t/rfc-scalable-vectorisation-in-linalg/
Differential Revision: https://reviews.llvm.org/D154336
This patch enables specifying scalable vector sizes when using the
Transform dialect to drive vectorisation, e.g.:
```
transform.structured.masked_vectorize %0 vector_sizes [8, 16, [4]]
```
This is implemented by extending the MaskedVectorizeOp with a dedicated
attribute for "scalability" and by overloading `parseDynamicIndexList`
so that MaskedVectorizeOp can continue using the auto-generated parser
and printer.
At the moment, only the trailing vec size can be scalable. The following
is not yet supported:
```
transform.structured.masked_vectorize %0 vector_sizes [8, [16], [4]]
```
As the vectoriser does not support scalable vectorisation just yet, a
warning is issues when scalable vector sizes are used. You can also use
the debug output, `--debug-only=linalg-vectorization`, to check whether
scalable vectorisation has been switched on.
This change is a part of a larger effort to enable scalable
vectorisation in Linalg. See this RFC for more context:
* https://discourse.llvm.org/t/rfc-scalable-vectorisation-in-linalg/
Similar patch for tiling: https://reviews.llvm.org/D150944
Differential Revision: https://reviews.llvm.org/D151892
This patch enables specifying scalable tile sizes when using the
Transform dialect to drive tiling, e.g.:
```
%1, %loop = transform.structured.tile %0 [[4]]
```
This is implemented by extending the TileOp with a dedicated attribute
for "scalability" and by updating various parsing hooks. At the moment,
only the trailing tile size can be scalable. The following is not yet
supported:
```
%1, %loop = transform.structured.tile %0 [[4], [4]]
```
This change is a part of larger effort to enable scalable vectorisation
in Linalg. See this RFC for more context:
* https://discourse.llvm.org/t/rfc-scalable-vectorisation-in-linalg/
Differential Revision: https://reviews.llvm.org/D150944
Types have been introduced a while ago and provide for better
readability and transform-time verification. Use them in the ops from
the structured transform dialect extension.
In most cases, the types are appended as trailing functional types or a
derived format of the functional type that allows for an empty right
hand size without the annoying `-> ()` syntax (similarly to `func.func`
declaration that may omit the arrow). When handles are used inside mixed
static/dynamic lists, such as tile sizes, types of those handles follow
them immediately as in `sizes [%0 : !transform.any_value, 42]`. This
allows for better readability than matching the trailing type.
Update code to remove hardcoded PDL dependencies and expunge PDL from
structured transform op code.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D144515
Previously we only allowed the flattened list passed in, but the same
input provided here as to buildGeneric so flatten accordingly. We have
less info here than in buildGeneric so the error is more generic if
unpacking fails.
Differential Revision: https://reviews.llvm.org/D143240
We can use `parseCommaSeparatedList` to simplify the logic of
`parseDynamicIndexList`. We don't need to explicitly check delimiters
and comma anymore, this is done for us by `parseCommaSeparatedList`.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D138694
This reverts commit 5711957875738c1318f89afd7bf4be388f85a087.
A circular dependency is introduced here from Dialect/Utils/ to the
ViewLikeInterface, but it already depends on Dialect/Utils.
Also this introduces a dependency from lib/Dialect/Tensor to Linalg,
which isn't obviously correct from a layering point of view.
This change adds a set of utilities to replace the result of a
`tensor.collapse_shape -> tensor.extract_slice` chain with the
equivalent result formed by aggregating slices of the
`tensor.collapse_shape` source. In general, it is not possible to
commute `extract_slice` and `collapse_shape` if linearized dimensions
are sliced. The i-th dimension of the `tensor.collapse_shape`
result is a "linearized sliced dimension" if:
1) Reassociation indices of tensor.collapse_shape in the i'th position
is greater than size 1 (multiple dimensions of the input are collapsed)
2) The i-th dimension is sliced by `tensor.extract_slice`.
We can work around this by stitching together the result of
`tensor.extract_slice` by iterating over any linearized sliced dimensions.
This is equivalent to "tiling" the linearized-and-sliced dimensions of
the `tensor.collapse_shape` operation in order to manifest the result
tile (the result of the `tensor.extract_slice`). The user of the
utilities must provide the mechanism to create the tiling (e.g. a loop).
In the tests, it is demonstrated how to apply the utilities using either
`scf.for` or `scf.foreach_thread`.
The below example illustrates the pattern using `scf.for`:
```
%0 = linalg.generic ... -> tensor<3x7x11x10xf32>
%1 = tensor.collapse_shape %0 [[0, 1, 2], [3]] : ... to tensor<341x10xf32>
%2 = tensor.extract_slice %1 [13, 0] [10, 10] [2, 1] : .... tensor<10x10xf32>
```
We can construct %2 by generating the following IR:
```
%dest = linalg.init_tensor() : tensor<10x10xf32>
%2 = scf.for %iv = %c0 to %c10 step %c1 iter_args(%arg0) -> tensor<10x10xf32> {
// Step 1: Map this output idx (%iv) to a multi-index for the input (%3):
%linear_index = affine.apply affine_map<(d0)[]->(d0*2 + 11)>(%iv)
%3:3 = arith.delinearize_index %iv into (3, 7, 11)
// Step 2: Extract the slice from the input
%4 = tensor.extract_slice %0 [%3#0, %3#1, %3#2, 0] [1, 1, 1, 10] [1, 1, 1, 1] :
tensor<3x7x11x10xf32> to tensor<1x1x1x10xf32>
%5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] :
tensor<1x1x1x10xf32> into tensor<1x10xf32>
// Step 3: Insert the slice into the destination
%6 = tensor.insert_slice %5 into %arg0 [%iv, 0] [1, 10] [1, 1] :
tensor<1x10xf32> into tensor<10x10xf32>
scf.yield %6 : tensor<10x10xf32>
}
```
The pattern was discussed in the RFC here: https://discourse.llvm.org/t/rfc-tensor-extracting-slices-from-tensor-collapse-shape/64034
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D129699
This patch adds support for string literals as `custom` directive
arguments. This can be useful for re-using custom parsers and printers
when arguments have a known value. For example:
```
ParseResult parseTypedAttr(AsmParser &parser, Attribute &attr, Type type) {
return parser.parseAttribute(attr, type);
}
void printTypedAttr(AsmPrinter &printer, Attribute attr, Type type) {
return parser.printAttributeWithoutType(attr);
}
```
And in TableGen:
```
def FooOp : ... {
let arguments = (ins AnyAttr:$a);
let assemblyFormat = [{ custom<TypedAttr>($a, "$_builder.getI1Type()")
attr-dict }];
}
def BarOp : ... {
let arguments = (ins AnyAttr:$a);
let assemblyFormat = [{ custom<TypedAttr>($a, "$_builder.getIndexType()")
attr-dict }];
}
```
Instead of writing two separate sets of custom parsers and printers.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D131603
The functions are effectively independent of the interface already, however, they take it as an argument for no reason.
The current state complicates reuse outside of MLIR.
Differential Revision: https://reviews.llvm.org/D131120
Add the reverse functions to the ViewLikeInterface's functions
`getMixedStrides`, `getMixedSizes`, and `getMixedOffsets`. The new functions
are useful to build view-like operations from an array of mixed static/dynamic
values.
Differential Revision: https://reviews.llvm.org/D128376
I am not sure about the meaning of Type in the name (was it meant be interpreted as Kind?), and given the importance and meaning of Type in the context of MLIR, its probably better to rename it. Given the comment in the source code, the suggestion in the GitHub issue and the final discussions in the review, this patch renames the OperandType to UnresolvedOperand.
Fixes https://github.com/llvm/llvm-project/issues/54446
Differential Revision: https://reviews.llvm.org/D122142
* This is useful when you need to build mixed array from external static/dynamic arrays (e.g. from adaptor during dialect conversion)
* Also, to reduce C++ code in td and generated files
Differential Revision: https://reviews.llvm.org/D117106
This revision fixes SubviewOp, InsertSliceOp, ExtractSliceOp construction during bufferization
where not all offset/size/stride operands were properly specified.
A test that exhibited problematic behaviors related to incorrect memref casts is introduced.
Init tensor optimization is disabled in teh testing func bufferize pass.
Differential Revision: https://reviews.llvm.org/D116899
The semantics of the ops that implement the
`OffsetSizeAndStrideOpInterface` is that if the number of offsets,
sizes or strides are less than the rank of the source, then some
default values are filled along the trailing dimensions (0 for offset,
source dimension of sizes, and 1 for strides). This is confusing,
especially with rank-reducing semantics. Immediate issue here is that
the methods of `OffsetSizeAndStridesOpInterface` assumes that the
number of values is same as the source rank. This cause out-of-bounds
errors.
So simplifying the specification of `OffsetSizeAndStridesOpInterface`
to make it invalid to specify number of offsets/sizes/strides not
equal to the source rank.
Differential Revision: https://reviews.llvm.org/D115677
This revision refactors and simplifies the pattern detection logic: thanks to SSA value properties, we can actually look at all the uses of a given value and avoid having to pattern-match specific chains of operations.
A bufferization pattern for subtensor is added and specific inplaceability analysis is implemented for the simple case of subtensor. More advanced use cases will follow.
Differential revision: https://reviews.llvm.org/D102512
Add printer and parser hooks for a custom directive that allows
parsing and printing of idioms that can represent a list of values
each of which is either an integer or an SSA value. For example in
`subview %source[%offset_0, 1] [4, %size_1] [%stride_0, 3]`
each of the list (which represents offset, size and strides) is a mix
of either statically know integer values or dynamically computed SSA
values. Since this is used in many places adding a custom directive to
parse/print this idiom allows using assembly format on operations
which use this idiom.
Differential Revision: https://reviews.llvm.org/D95773
OffsetSizeAndStrideOpInterface now have the ability to specify only a leading subset of
offset, sizes, strides operands/attributes.
The size of that leading subset must be limited by the corresponding entry in `getArrayAttrMaxRanks` to avoid overflows.
Missing trailing dimensions are assumed to span the whole range (i.e. [0 .. dim)).
This brings more natural semantics to slice-like op on top of subview and is a simplifies to removing all uses of SliceOp in dependent projects.
Differential revision: https://reviews.llvm.org/D95441
This operation is used to materialize a tensor of a particular
shape. The shape could be specified as a mix of static and dynamic
values.
The use of this operation is to be an `init` tensor for Linalg
structured operation on tensors where the bounds of the computation
depends on the shape of the output of the linalg operation. The result
of this operation will be used as the `init` tensor of such Linalg
operations. To note,
1) The values in the tensor materialized is not used. Any operation to
which this is an init tensor is expected to overwrite the entire
tensor.
2) The tensor is materialized only for the shape of the output and to
make the loop bounds depend only on operands of the structured
operation.
Based on (1) and (2) it is assumed that these operations eventually go
away since they are only used in `dim` operations that can be
canonicalized to make this operation dead. Such canonicalization are
added here too.
Differential Revision: https://reviews.llvm.org/D93374
Print part of an op of the form:
```
<optional-offset-prefix>`[` offset-list `]`
<optional-size-prefix>`[` size-list `]`
<optional-stride-prefix>[` stride-list `]`
```
Also address some leftover nits.
Differential revision: https://reviews.llvm.org/D92031
Parse trailing part of an op of the form:
```
<optional-offset-prefix>`[` offset-list `]`
<optional-size-prefix>`[` size-list `]`
<optional-stride-prefix>[` stride-list `]`
```
Each entry in the offset, size and stride list either resolves to an integer
constant or an operand of index type.
Constants are added to the `result` as named integer array attributes with
name `OffsetSizeAndStrideOpInterface::getStaticOffsetsAttrName()` (resp.
`getStaticSizesAttrName()`, `getStaticStridesAttrName()`).
Append the number of offset, size and stride operands to `segmentSizes`
before adding it to `result` as the named attribute:
`OpTrait::AttrSizedOperandSegments<void>::getOperandSegmentSizeAttr()`.
Offset, size and stride operands resolution occurs after `preResolutionFn`
to give a chance to leading operands to resolve first, after parsing the
types.
```
ParseResult parseOffsetsSizesAndStrides(
OpAsmParser &parser, OperationState &result, ArrayRef<int> segmentSizes,
llvm::function_ref<ParseResult(OpAsmParser &, OperationState &)>
preResolutionFn = nullptr,
llvm::function_ref<ParseResult(OpAsmParser &)> parseOptionalOffsetPrefix =
nullptr,
llvm::function_ref<ParseResult(OpAsmParser &)> parseOptionalSizePrefix =
nullptr,
llvm::function_ref<ParseResult(OpAsmParser &)> parseOptionalStridePrefix =
nullptr);
```
Differential revision: https://reviews.llvm.org/D92030
This revision will make it easier to create new ops base on the strided memref abstraction outside of the std dialect.
OffsetSizeAndStrideOpInterface is an interface for ops that allow specifying mixed dynamic and static offsets, sizes and strides variadic operands.
Ops that implement this interface need to expose the following methods:
1. `getArrayAttrRanks` to specify the length of static integer
attributes.
2. `offsets`, `sizes` and `strides` variadic operands.
3. `static_offsets`, resp. `static_sizes` and `static_strides` integer
array attributes.
The invariants of this interface are:
1. `static_offsets`, `static_sizes` and `static_strides` have length
exactly `getArrayAttrRanks()`[0] (resp. [1], [2]).
2. `offsets`, `sizes` and `strides` have each length at most
`getArrayAttrRanks()`[0] (resp. [1], [2]).
3. if an entry of `static_offsets` (resp. `static_sizes`,
`static_strides`) is equal to a special sentinel value, namely
`ShapedType::kDynamicStrideOrOffset` (resp. `ShapedType::kDynamicSize`,
`ShapedType::kDynamicStrideOrOffset`), then the corresponding entry is
a dynamic offset (resp. size, stride).
4. a variadic `offset` (resp. `sizes`, `strides`) operand must be present
for each dynamic offset (resp. size, stride).
This interface is useful to factor out common behavior and provide support
for carrying or injecting static behavior through the use of the static
attributes.
Differential Revision: https://reviews.llvm.org/D92011
