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 revision starts evolving the APIs to manipulate ops with offsets, sizes and operands towards a ValueOrAttr abstraction that is already used in folding under the name OpFoldResult.
The objective, in the future, is to allow such manipulations all the way to the level of ODS to avoid all the genuflexions involved in distinguishing between values and attributes for generic constant foldings.
Once this evolution is accepted, the next step will be a mechanical OpFoldResult -> ValueOrAttr.
Differential Revision: https://reviews.llvm.org/D95310
This transformation anchors on a padding op whose result is only used as an input
to a Linalg op and pulls it out of a given number of loops.
The result is a packing of padded tailes of ops that is amortized just before
the outermost loop from which the pad operation is hoisted.
Differential revision: https://reviews.llvm.org/D95243
This revision allows the base Linalg tiling pattern to optionally require padding to
a constant bounding shape.
When requested, a simple analysis is performed, similar to buffer promotion.
A temporary `linalg.simple_pad` op is added to model padding for the purpose of
connecting the dots. This will be replaced by a more fleshed out `linalg.pad_tensor`
op when it is available.
In the meantime, this temporary op serves the purpose of exhibiting the necessary
properties required from a more fleshed out pad op, to compose with transformations
properly.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D95149
A cast-like operation is one that converts from a set of input types to a set of output types. The arity of the inputs may be from 0-N, whereas the arity of the outputs may be anything from 1-N. Cast-like operations are removable in cases where they produce a "no-op", i.e when the input types and output types match 1-1.
Differential Revision: https://reviews.llvm.org/D94831
Like SubView, SubTensor/SubTensorInsertOp are allowed to have rank-reducing/expanding semantics. In the case of SubTensorInsertOp , the rank of offsets/sizes/strides should be the rank of the destination tensor.
Also, add a builder flavor for SubTensorOp to return a rank-reduced tensor.
Differential Revision: https://reviews.llvm.org/D95076
This revision drops init_tensor arguments from Linalg on tensors and instead uniformizes the output buffers and output tensors to be consistent.
This significantly simplifies the usage of Linalg on tensors and is a stepping stone for
its evolution towards a mixed tensor and shape abstraction discussed in https://llvm.discourse.group/t/linalg-and-shapes/2421/19.
Differential Revision: https://reviews.llvm.org/D93469
This better matches the rest of the infrastructure, is much simpler, and makes it easier to move these types to being declaratively specified.
Differential Revision: https://reviews.llvm.org/D93432
This reverts commit 0d48d265db.
This reapplies the following commit, with a fix for CAPI/ir.c:
[mlir] Start splitting the `tensor` dialect out of `std`.
This starts by moving `std.extract_element` to `tensor.extract` (this
mirrors the naming of `vector.extract`).
Curiously, `std.extract_element` supposedly works on vectors as well,
and this patch removes that functionality. I would tend to do that in
separate patch, but I couldn't find any downstream users relying on
this, and the fact that we have `vector.extract` made it seem safe
enough to lump in here.
This also sets up the `tensor` dialect as a dependency of the `std`
dialect, as some ops that currently live in `std` depend on
`tensor.extract` via their canonicalization patterns.
Part of RFC: https://llvm.discourse.group/t/rfc-split-the-tensor-dialect-from-std/2347/2
Differential Revision: https://reviews.llvm.org/D92991
This starts by moving `std.extract_element` to `tensor.extract` (this
mirrors the naming of `vector.extract`).
Curiously, `std.extract_element` supposedly works on vectors as well,
and this patch removes that functionality. I would tend to do that in
separate patch, but I couldn't find any downstream users relying on
this, and the fact that we have `vector.extract` made it seem safe
enough to lump in here.
This also sets up the `tensor` dialect as a dependency of the `std`
dialect, as some ops that currently live in `std` depend on
`tensor.extract` via their canonicalization patterns.
Part of RFC: https://llvm.discourse.group/t/rfc-split-the-tensor-dialect-from-std/2347/2
Differential Revision: https://reviews.llvm.org/D92991
This was missed when supported for unsigned/signed integer types was first added, and results in crashes if a user tries to create/print a constant with the incorrect integer type.
Fixes PR#46222
Differential Revision: https://reviews.llvm.org/D92981
This is part of a larger refactoring the better congregates the builtin structures under the BuiltinDialect. This also removes the problematic "standard" naming that clashes with the "standard" dialect, which is not defined within IR/. A temporary forward is placed in StandardTypes.h to allow time for downstream users to replaced references.
Differential Revision: https://reviews.llvm.org/D92435
Given that OpState already implicit converts to Operator*, this seems reasonable.
The alternative would be to add more functions to OpState which forward to Operation.
Reviewed By: rriddle, ftynse
Differential Revision: https://reviews.llvm.org/D92266
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
This canonicalization is useful to resolve loads into scalar values when
doing partial bufferization.
Differential Revision: https://reviews.llvm.org/D91855
The shape of the result of a dynamic_tensor_from_elements is defined via its
result type and operands. We already fold dim operations when they reference
one of the statically sized dimensions. Now, also fold dim on the dynamically
sized dimensions by picking the corresponding operand.
Differential Revision: https://reviews.llvm.org/D91616
These includes have been deprecated in favor of BuiltinDialect.h, which contains the definitions of ModuleOp and FuncOp.
Differential Revision: https://reviews.llvm.org/D91572
There exists a generic folding facility that folds the operand of a memref_cast
into users of memref_cast that support this. However, it was not used for the
memref_cast itself. Fix it to enable elimination of memref_cast chains such as
%1 = memref_cast %0 : A to B
%2 = memref_cast %1 : B to A
that is achieved by combining the folding with the existing "A to A" cast
elimination.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D90910
- Add standard dialect operations to define global variables with memref types and to
retrieve the memref for to a named global variable
- Extend unit tests to test verification for these operations.
Differential Revision: https://reviews.llvm.org/D90337
Often times the legality of inlining can change depending on if the callable is going to be inlined in-place, or cloned. For example, some operations are not allowed to be duplicated and can only be inlined if the original callable will cease to exist afterwards. The new `wouldBeCloned` flag allows for dialects to hook into this when determining legality.
Differential Revision: https://reviews.llvm.org/D90360
In certain situations it isn't legal to inline a call operation, but this isn't something that is possible(at least not easily) to prevent with the current hooks. This revision adds a new hook so that dialects with call operations that shouldn't be inlined can prevent it.
Differential Revision: https://reviews.llvm.org/D90359
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
This simplifies a lot of handling of BoolAttr/IntegerAttr. For example, a lot of places currently have to handle both IntegerAttr and BoolAttr. In other places, a decision is made to pick one which can lead to surprising results for users. For example, DenseElementsAttr currently uses BoolAttr for i1 even if the user initialized it with an Array of i1 IntegerAttrs.
Differential Revision: https://reviews.llvm.org/D81047
The subview semantics changes recently to allow for more natural
representation of constant offsets and strides. The legalization of
subview op for lowering to SPIR-V needs to account for this.
Also change the linearization to use the strides from the affine map
of a memref.
Differential Revision: https://reviews.llvm.org/D80270
DimOp folding is using bare accesses to underlying SubViewOp operands.
This is generally incorrect and is fixed in this revision.
Differential Revision: https://reviews.llvm.org/D80017
The existing implementation of SubViewOp::getRanges relies on all
offsets/sizes/strides to be dynamic values and does not work in
combination with canonicalization. This revision adds a
SubViewOp::getOrCreateRanges to create the missing constants in the
canonicalized case.
This allows reactivating the fused pass with staged pattern
applications.
However another issue surfaces that the SubViewOp verifier is now too
strict to allow folding. The existing folding pattern is turned into a
canonicalization pattern which rewrites memref_cast + subview into
subview + memref_cast.
The transform-patterns-matmul-to-vector can then be reactivated.
Differential Revision: https://reviews.llvm.org/D79759
The main objective of this revision is to change the way static information is represented, propagated and canonicalized in the SubViewOp.
In the current implementation the issue is that canonicalization may strictly lose information because static offsets are combined in irrecoverable ways into the result type, in order to fit the strided memref representation.
The core semantics of the op do not change but the parser and printer do: the op always requires `rank` offsets, sizes and strides. These quantities can now be either SSA values or static integer attributes.
The result type is automatically deduced from the static information and more powerful canonicalizations (as powerful as the representation with sentinel `?` values allows). Previously static information was inferred on a best-effort basis from looking at the source and destination type.
Relevant tests are rewritten to use the idiomatic `offset: x, strides : [...]`-form. Bugs are corrected along the way that were not trivially visible in flattened strided memref form.
Lowering to LLVM is updated, simplified and now supports all cases.
A mixed static-dynamic mode test that wouldn't previously lower is added.
It is an open question, and a longer discussion, whether a better result type representation would be a nicer alternative. For now, the subview op carries the required semantic.
Differential Revision: https://reviews.llvm.org/D79662
This reverts commit 80d133b24f.
Per Stephan Herhut: The canonicalizer pattern that was added creates
forms of the subview op that cannot be lowered.
This is shown by failing Tensorflow XLA tests such as:
tensorflow/compiler/xla/service/mlir_gpu/tests:abs.hlo.test
Will provide more details offline, they rely on logs from private CI.
Summary:
The main objective of this revision is to change the way static information is represented, propagated and canonicalized in the SubViewOp.
In the current implementation the issue is that canonicalization may strictly lose information because static offsets are combined in irrecoverable ways into the result type, in order to fit the strided memref representation.
The core semantics of the op do not change but the parser and printer do: the op always requires `rank` offsets, sizes and strides. These quantities can now be either SSA values or static integer attributes.
The result type is automatically deduced from the static information and more powerful canonicalizations (as powerful as the representation with sentinel `?` values allows). Previously static information was inferred on a best-effort basis from looking at the source and destination type.
Relevant tests are rewritten to use the idiomatic `offset: x, strides : [...]`-form. Bugs are corrected along the way that were not trivially visible in flattened strided memref form.
It is an open question, and a longer discussion, whether a better result type representation would be a nicer alternative. For now, the subview op carries the required semantic.
Reviewers: ftynse, mravishankar, antiagainst, rriddle!, andydavis1, timshen, asaadaldien, stellaraccident
Reviewed By: mravishankar
Subscribers: aartbik, bondhugula, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, arpith-jacob, mgester, lucyrfox, liufengdb, stephenneuendorffer, Joonsoo, bader, grosul1, frgossen, Kayjukh, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D79662
Summary:
This makes a common pattern of
`dyn_cast_or_null<OpTy>(v.getDefiningOp())` more concise.
Differential Revision: https://reviews.llvm.org/D79681
This [discussion](https://llvm.discourse.group/t/viewop-isnt-expressive-enough/991/2) raised some concerns with ViewOp.
In particular, the handling of offsets is incorrect and does not match the op description.
Note that with an elemental type change, offsets cannot be part of the type in general because sizeof(srcType) != sizeof(dstType).
Howerver, offset is a poorly chosen term for this purpose and is renamed to byte_shift.
Additionally, for all intended purposes, trying to support non-identity layouts for this op does not bring expressive power but rather increases code complexity.
This revision simplifies the existing semantics and implementation.
This simplification effort is voluntarily restrictive and acts as a stepping stone towards supporting richer semantics: treat the non-common cases as YAGNI for now and reevaluate based on concrete use cases once a round of simplification occurred.
Differential revision: https://reviews.llvm.org/D79541
Nicolas Vasilache