This is the second (and final) step of making "destination style" usable without depending on the Linalg dialect. (The first step was D135129.)
This change allows us to provide default bufferization implementations for all destination-style ops. It also allows us to simplify `TilingInterface`. (E.g., `getDestinationOperands` can be removed.)
Differential Revision: https://reviews.llvm.org/D136179
This assert is erroneous because an op implementing
`RegionBranchOpInterface` can have variadic regions and in some cases
have zero regions, in which case the only possible control flow is
branching from the parent op to itself.
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D136052
This patch takes the first step towards a more principled modeling of undefined behavior in MLIR as discussed in the following discourse threads:
1. https://discourse.llvm.org/t/semantics-modeling-undefined-behavior-and-side-effects/4812
2. https://discourse.llvm.org/t/rfc-mark-tensor-dim-and-memref-dim-as-side-effecting/65729
This patch in particular does the following:
1. Introduces a ConditionallySpeculatable OpInterface that dynamically determines whether an Operation can be speculated.
2. Re-defines `NoSideEffect` to allow undefined behavior, making it necessary but not sufficient for speculation. Also renames it to `NoMemoryEffect`.
3. Makes LICM respect the above semantics.
4. Changes all ops tagged with `NoSideEffect` today to additionally implement ConditionallySpeculatable and mark themselves as always speculatable. This combined trait is named `Pure`. This makes this change NFC.
For out of tree dialects:
1. Replace `NoSideEffect` with `Pure` if the operation does not have any memory effects, undefined behavior or infinite loops.
2. Replace `NoSideEffect` with `NoSideEffect` otherwise.
The next steps in this process are (I'm proposing to do these in upcoming patches):
1. Update operations like `tensor.dim`, `memref.dim`, `scf.for`, `affine.for` to implement a correct hook for `ConditionallySpeculatable`. I'm also happy to update ops in other dialects if the respective dialect owners would like to and can give me some pointers.
2. Update other passes that speculate operations to consult `ConditionallySpeculatable` in addition to `NoMemoryEffect`. I could not find any other than LICM on a quick skim, but I could have missed some.
3. Add some documentation / FAQs detailing the differences between side effects, undefined behavior, speculatabilty.
Reviewed By: rriddle, mehdi_amini
Differential Revision: https://reviews.llvm.org/D135505
This patch introduces the `vector.mask` operation and the MaskableOpInterface
as described in https://discourse.llvm.org/t/rfc-vector-masking-representation-in-mlir/64964.
The `vector.mask` operation is used to predicate the execution of operations
implementing the MaskableOpInterface. This interface will be implemented by maskable
operations and provides information about its masking constraints and semantics.
For now, only vector transfer and reduction ops implement the MaskableOpInterface
for illustration and testing purposes.
Reviewed By: nicolasvasilache, rriddle
Differential Revision: https://reviews.llvm.org/D134939
This interface is implemented by memref.dim and tensor.dim. This change makes it possible to remove a build dependency of the Affine dialect on the Tensor dialect (and maybe also the MemRef dialect in the future).
Differential Revision: https://reviews.llvm.org/D133595
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
In the current state, this is only special cased for Allocation effects, but any effects on results allocated by the operation may be ignored when checking whether the op may be removed, as none of them are possible to be observed if the result is unused.
A use case for this is for IRs for languages which always initialize on allocation. To correctly model such operations, a Write as well as an Allocation effect should be placed on the result. This would prevent the Op from being deleted if unused however. This patch fixes that issue.
Differential Revision: https://reviews.llvm.org/D129854
refineReturnType method shares the same parameters as inferReturnTypes
but gets passed in the return types of the op if known that can be used
during refinement passes or for more op specific error reporting.
Currently the error reporting on failure is generic and doesn't allow
for specializing the returned result based on failure, with this change
what would previously have been a separate trait with specialized
verification can just be handled as part of inferrence rather than
duplicated.
refineReturnTypes behaves like inferReturnTypes if no result types are fed in,
while the current verification is recast as the default implementation for
refineReturnTypes with it calling inferReturnTypes (and so the default type
verification now goes through refine and allows for more op specific inference
mismatch errors).
Differential Revision: https://reviews.llvm.org/D129955
Clean up checks for alloc-like ops in analysis. Use the analysis
utility to properly check for the desired kind of effects. The previous
locality utility worked for all practical purposes but wasn't sound and
was locally duplicate code. Instead, use mlir::hasSingleEffect.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D129439
This allows purging references of scf.ForeachThreadOp and scf.PerformConcurrentlyOp from
ParallelInsertSliceOp.
This will allowmoving the op closer to tensor::InsertSliceOp with which it should share much more
code.
In the future, the decoupling will also allow extending the type of ops that can be used in the
parallel combinator as well as semantics related to multiple concurrent inserts to the same
result.
Differential Revision: https://reviews.llvm.org/D128857
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
Ops that implement `RegionBranchOpInterface` are allowed to indicate that they can branch back to themselves in `getSuccessorRegions`, but there is no API that allows them to specify the forwarded operands. This patch enables that by changing `getSuccessorEntryOperands` to accept `None`.
Fixes#54928
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D127239
When `RegionBranchOpInterface::getSuccessorRegions` is called for anything other than the parent op, it expects the operands of the terminator of the source region to be passed, not the operands of the parent op. This was not always respected.
This fixes a bug in integer range inference and ForwardDataFlowSolver and changes `scf.while` to allow narrowing of successors using constant inputs.
Fixes#55873
Reviewed By: mehdi_amini, krzysz00
Differential Revision: https://reviews.llvm.org/D127261
This reverts commit 4e5ce2056e3e85f109a074e80bdd23a10ca2bed9.
This relands commit 1350c9887dca5ba80af8e3c1e61b29d6696eb240.
Reinstates the range analysis with the build issue fixed.
Differential Revision: https://reviews.llvm.org/D126926
This commit defines a dataflow analysis for integer ranges, which
uses a newly-added InferIntRangeInterface to compute the lower and
upper bounds on the results of an operation from the bounds on the
arguments. The range inference is a flow-insensitive dataflow analysis
that can be used to simplify code, such as by statically identifying
bounds checks that cannot fail in order to eliminate them.
The InferIntRangeInterface has one method, inferResultRanges(), which
takes a vector of inferred ranges for each argument to an op
implementing the interface and a callback allowing the implementation
to define the ranges for each result. These ranges are stored as
ConstantIntRanges, which hold the lower and upper bounds for a
value. Bounds are tracked separately for the signed and unsigned
interpretations of a value, which ensures that the impact of
arithmetic overflows is correctly tracked during the analysis.
The commit also adds a -test-int-range-inference pass to test the
analysis until it is integrated into SCCP or otherwise exposed.
Finally, this commit fixes some bugs relating to the handling of
region iteration arguments and terminators in the data flow analysis
framework.
Depends on D124020
Depends on D124021
Reviewed By: rriddle, Mogball
Differential Revision: https://reviews.llvm.org/D124023
Erase gpu.memcpy op when only uses of dest are
the memcpy op in question, its allocation and deallocation
ops.
Reviewed By: bondhugula, csigg
Differential Revision: https://reviews.llvm.org/D124257
Add helper functions to check if an op may be executed multiple times based on RegionBranchOpInterface.
Differential Revision: https://reviews.llvm.org/D123789
Changes the algorithm of LICM to support graph regions (no guarantee of topologically sorted order). Also fixes an issue where ops with recursive side effects and regions would not be hoisted if any nested ops used operands that were defined within the nested region.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D122465
This patch revamps the BranchOpInterface a bit and allows a proper implementation of what was previously `getMutableSuccessorOperands` for operations, which internally produce arguments to some of the block arguments. A motivating example for this would be an invoke op with a error handling path:
```
invoke %function(%0)
label ^success ^error(%1 : i32)
^error(%e: !error, %arg0 : i32):
...
```
The advantages of this are that any users of `BranchOpInterface` can still argue over remaining block argument operands (such as `%1` in the example above), as well as make use of the modifying capabilities to add more operands, erase an operand etc.
The way this patch implements that functionality is via a new class called `SuccessorOperands`, which is now returned by `getSuccessorOperands`. It basically contains an `unsigned` denoting how many operator produced operands exist, as well as a `MutableOperandRange`, which are the usual forwarded operands we are used to. The produced operands are assumed to the first few block arguments, followed by the forwarded operands afterwards. The role of `SuccessorOperands` is to provide various utility functions to modify and query the successor arguments from a `BranchOpInterface`.
Differential Revision: https://reviews.llvm.org/D123062
- Adds default implementations of `isDefinedOutsideOfLoop` and `moveOutOfLoop` since 99% of all implementations of these functions were identical
- `moveOutOfLoop` takes one operation and doesn't return anything anymore. 100% of all implementations of this function would always return `success` and uses would either respond with a pass failure or an `llvm_unreachable`.
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
RegionBranchOpInterface and BranchOpInterface are allowed to make implicit type conversions along control-flow edges. In effect, this adds an interface method, `areTypesCompatible`, to both interfaces, which should return whether the types of corresponding successor operands and block arguments are compatible. Users of the interfaces, here on forth, must be aware that types may mismatch, although current users (in MLIR core), are not affected by this change. By default, type equality is used.
`async.execute` already has unequal types along control-flow edges (`!async.value<f32>` vs. `f32`), but it opted out of calling `RegionBranchOpInterface::verifyTypes` in its verifier. That method has now been removed and `RegionBranchOpInterface` will verify types along control edges by default in its verifier.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D120790
Add support for integer and float types into the data layout subsystem with
default logic similar to LLVM IR. Given the flexibility of the sybsystem, the
logic can be easily overwritten by operations if necessary. This provides the
connection necessary, e.g., for the GPU target where alignment requirements for
integers and floats differ from those provided by default (although still
compatible with the LLVM IR model). Previously, it was impossible to use
non-default alignment requirements for integer and float types, which could
lead to incorrect address and size calculations when targeting GPUs.
Depends On D120737
Reviewed By: wsmoses
Differential Revision: https://reviews.llvm.org/D120739
This matches the same API usage as attributes/ops/types. For example:
```c++
Dialect *dialect = ...;
// Instead of this:
if (auto *interface = dialect->getRegisteredInterface<DialectInlinerInterface>())
// You can do this:
if (auto *interface = dyn_cast<DialectInlinerInterface>(dialect))
```
Differential Revision: https://reviews.llvm.org/D117859
