When all operands or results are variadic, zero values is a perfectly valid behavior
to expect, and we shouldn't force the user to provide values in this case. For example,
when creating a call or a return operation we often don't want/need to provide return
values.
Differential Revision: https://reviews.llvm.org/D133721
Up until now PDL(L) has not supported dialect conversion because we had no
way of remapping values or integrating with type conversions. This commit
rectifies that by adding a new "pattern configuration" concept to PDL. This
essentially allows for attaching external configurations to patterns, which
can hook into pattern events (for now just the scope of a rewrite, but we
could also pass configs to native rewrites as well). This allows for injecting
the type converter into the conversion pattern rewriter.
Differential Revision: https://reviews.llvm.org/D133142
This change allows analyzing ops from different block, in particular when used in programs that have `cf` branches.
Differential Revision: https://reviews.llvm.org/D135644
Currently, for sparse analyses, we always store a `Optional<ValueT>` in each lattice element. When it's `None`, we consider the lattice element as `uninitialized`.
However:
* Not all lattices have an `uninitialized` state. For example, `Executable` and `PredecessorState` have default values so they are always initialized.
* In dense analyses, we don't have the concept of an `uninitialized` state.
Given these inconsistencies, this patch removes `Lattice::isUninitialized()`. Individual analysis states are now default-constructed. If the default state of an analysis can be considered as "uninitialized" then this analysis should implement the following logic:
* Special join rule: `join(uninitialized, any) == any`.
* Special bail out logic: if any of the input states is uninitialized, exit the transfer function early.
Depends On D132086
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D132800
This change add a helper function for computing a topological sorting of a list of ops. E.g. this can be useful in transforms where a subset of ops should be cloned without dominance errors.
The analysis reuses the existing implementation in TopologicalSortUtils.cpp.
Differential Revision: https://reviews.llvm.org/D131669
Added a commutativity utility pattern and a function to populate it. The pattern sorts the operands of an op in ascending order of the "key" associated with each operand iff the op is commutative. This sorting is stable.
The function is intended to be used inside passes to simplify the matching of commutative operations. After the application of the above-mentioned pattern, since the commutative operands now have a deterministic order in which they occur in an op, the matching of large DAGs becomes much simpler, i.e., requires much less number of checks to be written by a user in her/his pattern matching function.
The "key" associated with an operand is the list of the "AncestorKeys" associated with the ancestors of this operand, in a breadth-first order.
The operand of any op is produced by a set of ops and block arguments. Each of these ops and block arguments is called an "ancestor" of this operand.
Now, the "AncestorKey" associated with:
1. A block argument is `{type: BLOCK_ARGUMENT, opName: ""}`.
2. A non-constant-like op, for example, `arith.addi`, is `{type: NON_CONSTANT_OP, opName: "arith.addi"}`.
3. A constant-like op, for example, `arith.constant`, is `{type: CONSTANT_OP, opName: "arith.constant"}`.
So, if an operand, say `A`, was produced as follows:
```
`<block argument>` `<block argument>`
\ /
\ /
`arith.subi` `arith.constant`
\ /
`arith.addi`
|
returns `A`
```
Then, the block arguments and operations present in the backward slice of `A`, in the breadth-first order are:
`arith.addi`, `arith.subi`, `arith.constant`, `<block argument>`, and `<block argument>`.
Thus, the "key" associated with operand `A` is:
```
{
{type: NON_CONSTANT_OP, opName: "arith.addi"},
{type: NON_CONSTANT_OP, opName: "arith.subi"},
{type: CONSTANT_OP, opName: "arith.constant"},
{type: BLOCK_ARGUMENT, opName: ""},
{type: BLOCK_ARGUMENT, opName: ""}
}
```
Now, if "keyA" is the key associated with operand `A` and "keyB" is the key associated with operand `B`, then:
"keyA" < "keyB" iff:
1. In the first unequal pair of corresponding AncestorKeys, the AncestorKey in operand `A` is smaller, or,
2. Both the AncestorKeys in every pair are the same and the size of operand `A`'s "key" is smaller.
AncestorKeys of type `BLOCK_ARGUMENT` are considered the smallest, those of type `CONSTANT_OP`, the largest, and `NON_CONSTANT_OP` types come in between. Within the types `NON_CONSTANT_OP` and `CONSTANT_OP`, the smaller ones are the ones with smaller op names (lexicographically).
---
Some examples of such a sorting:
Assume that the sorting is being applied to `foo.commutative`, which is a commutative op.
Example 1:
> %1 = foo.const 0
> %2 = foo.mul <block argument>, <block argument>
> %3 = foo.commutative %1, %2
Here,
1. The key associated with %1 is:
```
{
{CONSTANT_OP, "foo.const"}
}
```
2. The key associated with %2 is:
```
{
{NON_CONSTANT_OP, "foo.mul"},
{BLOCK_ARGUMENT, ""},
{BLOCK_ARGUMENT, ""}
}
```
The key of %2 < the key of %1
Thus, the sorted `foo.commutative` is:
> %3 = foo.commutative %2, %1
Example 2:
> %1 = foo.const 0
> %2 = foo.mul <block argument>, <block argument>
> %3 = foo.mul %2, %1
> %4 = foo.add %2, %1
> %5 = foo.commutative %1, %2, %3, %4
Here,
1. The key associated with %1 is:
```
{
{CONSTANT_OP, "foo.const"}
}
```
2. The key associated with %2 is:
```
{
{NON_CONSTANT_OP, "foo.mul"},
{BLOCK_ARGUMENT, ""}
}
```
3. The key associated with %3 is:
```
{
{NON_CONSTANT_OP, "foo.mul"},
{NON_CONSTANT_OP, "foo.mul"},
{CONSTANT_OP, "foo.const"},
{BLOCK_ARGUMENT, ""},
{BLOCK_ARGUMENT, ""}
}
```
4. The key associated with %4 is:
```
{
{NON_CONSTANT_OP, "foo.add"},
{NON_CONSTANT_OP, "foo.mul"},
{CONSTANT_OP, "foo.const"},
{BLOCK_ARGUMENT, ""},
{BLOCK_ARGUMENT, ""}
}
```
Thus, the sorted `foo.commutative` is:
> %5 = foo.commutative %4, %3, %2, %1
Signed-off-by: Srishti Srivastava <srishti.srivastava@polymagelabs.com>
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D124750
Integer range inference has been swapped to the new framework. The integer value range lattices automatically updates the corresponding constant value on update.
Depends on D127173
Reviewed By: krzysz00, rriddle
Differential Revision: https://reviews.llvm.org/D128866
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
This commit restructures how TypeID is implemented to ideally avoid
the current problems related to shared libraries. This is done by changing
the "implicit" fallback path to use the name of the type, instead of using
a static template variable (which breaks shared libraries). The major downside to this
is that it adds some additional initialization costs for the implicit path. Given the
use of type names for uniqueness in the fallback, we also no longer allow types
defined in anonymous namespaces to have an implicit TypeID. To simplify defining
an ID for these classes, a new `MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID` macro
was added to allow for explicitly defining a TypeID directly on an internal class.
To help identify when types are using the fallback, `-debug-only=typeid` can be
used to log which types are using implicit ids.
This change generally only requires changes to the test passes, which are all defined
in anonymous namespaces, and thus can't use the fallback any longer.
Differential Revision: https://reviews.llvm.org/D122775
(This was a TODO from the initial patch).
The control-flow sink utility accepts a callback that is used to sink an operation into a region.
The `moveIntoRegion` is called on the same operation and region that return true for `shouldMoveIntoRegion`.
The callback must preserve the dominance of the operation within the region. In the default control-flow
sink implementation, this is moving the operation to the start of the entry block.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D122445
A lot of test passes are currently anchored on FuncOp, but this
dependency
is generally just historical. A majority of these test passes can run on
any operation, or can operate on a specific interface
(FunctionOpInterface/SymbolOpInterface).
This allows for greatly reducing the API dependency on FuncOp, which
is slated to be moved out of the Builtin dialect.
Differential Revision: https://reviews.llvm.org/D121191
The last remaining operations in the standard dialect all revolve around
FuncOp/function related constructs. This patch simply handles the initial
renaming (which by itself is already huge), but there are a large number
of cleanups unlocked/necessary afterwards:
* Removing a bunch of unnecessary dependencies on Func
* Cleaning up the From/ToStandard conversion passes
* Preparing for the move of FuncOp to the Func dialect
See the discussion at https://discourse.llvm.org/t/standard-dialect-the-final-chapter/6061
Differential Revision: https://reviews.llvm.org/D120624
This has been a major TODO for a very long time, and is necessary for establishing a proper
dialect-free dependency layering for the Transforms library. Code was moved to effectively
two main locations:
* Affine/
There was quite a bit of affine dialect related code in Transforms/ do to historical reasons
(of a time way into MLIR's past). The following headers were moved to:
Transforms/LoopFusionUtils.h -> Dialect/Affine/LoopFusionUtils.h
Transforms/LoopUtils.h -> Dialect/Affine/LoopUtils.h
Transforms/Utils.h -> Dialect/Affine/Utils.h
The following transforms were also moved:
AffineLoopFusion, AffinePipelineDataTransfer, LoopCoalescing
* SCF/
Only one SCF pass was in Transforms/ (likely accidentally placed here): ParallelLoopCollapsing
The SCF specific utilities in LoopUtils have been moved to SCF/Utils.h
* Misc:
mlir::moveLoopInvariantCode was also moved to LoopLikeInterface.h given
that it is a simple utility defined in terms of LoopLikeOpInterface.
Differential Revision: https://reviews.llvm.org/D117848
The only benefit of FunctionPass is that it filters out function
declarations. This isn't enough to justify carrying it around, as we can
simplify filter out declarations when necessary within the pass. We can
also explore with better scheduling primitives to filter out declarations
at the pipeline level in the future.
The definition of FunctionPass is left intact for now to allow time for downstream
users to migrate.
Differential Revision: https://reviews.llvm.org/D117182
The current state of the top level Analysis/ directory is that it contains two libraries;
a generic Analysis library (free from dialect dependencies), and a LoopAnalysis library
that contains various analysis utilities that originated from Affine loop transformations.
This commit moves the LoopAnalysis to the more appropriate home of `Dialect/Affine/Analysis/`,
given the use and intention of the majority of the code within it. After the move, if there
are generic utilities that would fit better in the top-level Analysis/ directory, we can move
them.
Differential Revision: https://reviews.llvm.org/D117351
Follow up to also use the prefixed emitters in OpFormatGen (moved
getGetterName(s) and getSetterName(s) to Operator as that is most
convenient usage wise even though it just depends on Dialect). Prefix
accessors in Test dialect and follow up on missed changes in
OpDefinitionsGen.
Differential Revision: https://reviews.llvm.org/D112118
Precursor: https://reviews.llvm.org/D110200
Removed redundant ops from the standard dialect that were moved to the
`arith` or `math` dialects.
Renamed all instances of operations in the codebase and in tests.
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D110797
Switches to adding target specific, private includes instead of adding
global includes.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D109494
While the changes are extensive, they basically fall into a few
categories:
1) Moving the TestDialect itself.
2) Updating C++ code in tablegen to explicitly use ::mlir, since it
will be put in a headers that shouldn't expect a 'using'.
3) Updating some generic MLIR Interface definitions to do the same thing.
4) Updating the Tablegen generator in a few places to be explicit about
namespaces
5) Doing the same thing for llvm references, since we no longer pick
up the definitions from mlir/Support/LLVM.h
Differential Revision: https://reviews.llvm.org/D88251
This can be useful when one needs to know which unrolled iteration an Op belongs to, for example, conveying noalias information among memory-affecting ops in parallel-access loops.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D107789
test/lib/Transforms/ has bitrot and become somewhat of a dumping grounds for testing pretty much any part of the project. This revision cleans this up, and moves the files within to a directory that reflects what is actually being tested.
Differential Revision: https://reviews.llvm.org/D102456
Instead of an SCF for loop, these pattern generate fully unrolled loops with no temporary buffer allocations.
Differential Revision: https://reviews.llvm.org/D101981
According to the API contract, LinalgLoopDistributionOptions
expects to work on parallel iterators. When getting processor
information, only loop ranges for parallel dimensions should
be fed in. But right now after generating scf.for loop nests,
we feed in *all* loops, including the ones materialized for
reduction iterators. This can cause unexpected distribution
of reduction dimensions. This commit fixes it.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D102079
This revision migrates more code from Linalg into the new permanent home of
SparseTensor. It replaces the test passes with proper compiler passes.
NOTE: the actual removal of the last glue and clutter in Linalg will follow
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D101811
Move TransposeOp lowering in its own populate function as in some cases
it is better to keep it during ContractOp lowering to better
canonicalize it rather than emiting scalar insert/extract.
Differential Revision: https://reviews.llvm.org/D101647
Three patterns are added to convert into vector.multi_reduction into a
sequence of vector.reduction as the following:
- Transpose the inputs so inner most dimensions are always reduction.
- Reduce rank of vector.multi_reduction into 2d with inner most
reduction dim (get the 2d canical form)
- 2D canonical form is converted into a sequence of vector.reduction.
There are two things we might worth in a follow up diff:
- An scf.for (maybe optionally) around vector.reduction instead of unrolling it.
- Breakdown the vector.reduction into a sequence of vector.reduction
(e.g tree-based reduction) instead of relying on how downstream dialects
handle it.
Note: this will requires passing target-vector-length
Differential Revision: https://reviews.llvm.org/D101570
This is the very first step toward removing the glue and clutter from linalg and
replace it with proper sparse tensor types. This revision migrates the LinalgSparseOps
into SparseTensorOps of a sparse tensor dialect. This also provides a new home for
sparse tensor related transformation.
NOTE: the actual replacement with sparse tensor types (and removal of linalg glue/clutter)
will follow but I am trying to keep the amount of changes per revision manageable.
Differential Revision: https://reviews.llvm.org/D101573
This is the very first step toward removing the glue and clutter from linalg and
replace it with proper sparse tensor types. This revision migrates the LinalgSparseOps
into SparseTensorOps of a sparse tensor dialect. This also provides a new home for
sparse tensor related transformation.
NOTE: the actual replacement with sparse tensor types (and removal of linalg glue/clutter)
will follow but I am trying to keep the amount of changes per revision manageable.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D101488
FillOp allows complex ops, and filling a properly sized buffer with
a default zero complex number is implemented.
Differential Revision: https://reviews.llvm.org/D99939
Add a new ProgressiveVectorToSCF pass that lowers vector transfer ops to SCF by gradually unpacking one dimension at time. Unpacking stops at 1D, but can be configured to stop earlier, should the HW support (N>1)-d vectors.
The current implementation cannot handle permutation maps, masks, tensor types and unrolling yet. These will be added in subsequent commits. Once features are on par with VectorToSCF, this implementation will replace VectorToSCF.
Differential Revision: https://reviews.llvm.org/D100622
Instead of interchanging loops during the loop lowering this pass performs the interchange by permuting the indexing maps. It also updates the iterator types and the index accesses in the body of the operation.
Differential Revision: https://reviews.llvm.org/D100627
