This updates the codebase to pass the context when creating an instance of
OwningRewritePatternList, and starts removing extraneous MLIRContext
parameters.  There are many many more to be removed.

Differential Revision: https://reviews.llvm.org/D99028

* Fold SelectOp when both true and false args are same SSA value
* Fold some cmp + select patterns

Differential Revision: https://reviews.llvm.org/D98576

* Do we need a threshold on maximum number of Yeild arguments processed (maximum number of SelectOp's to be generated)?
* Had to modify some old IfOp tests to not get optimized by this pattern

Differential Revision: https://reviews.llvm.org/D98592

This makes the annotation tied to the operand and the use of a keyword
more explicit/readable on what it means.

Differential Revision: https://reviews.llvm.org/D99001

Transforms.cpp:586:16: error: unused variable 'v' [-Werror,-Wunused-variable]
    for (Value v : operands)
               ^

https://llvm.discourse.group/t/rfc-add-linalg-tileop/2833

Differential Revision: https://reviews.llvm.org/D98900

This change combines for ROCm what was done for CUDA in D97463, D98203, D98360, and D98396.

I did not try to compile SerializeToHsaco.cpp or test mlir/test/Integration/GPU/ROCM because I don't have an AMD card. I fixed the things that had obvious bit-rot though.

Reviewed By: whchung

Differential Revision: https://reviews.llvm.org/D98447

This reverts commit c69550c1 with
proper fix applied.

This reverts commit 32a744ab.

CI is broken:

test/Dialect/Linalg/bufferize.mlir:274:12: error: CHECK: expected string not found in input
 // CHECK: %[[MEMREF:.*]] = tensor_to_memref %[[IN]] : memref<?xf32>
           ^

`BufferizeAnyLinalgOp` fails because `FillOp` is not a `LinalgGenericOp` and it fails while reading operand sizes attribute.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D98671

This propagates the affine map to transfer_read op in case it is not a
minor identity map.

Differential Revision: https://reviews.llvm.org/D98523

Also use `ArrayAttr` to pass iterator pass to the TiledLoopOp builder.

Differential Revision: https://reviews.llvm.org/D98871

Co-authored-by: Kiran Chandramohan <kiran.chandramohan@arm.com>

Reviewed By: ftynse

Differential Revision: https://reviews.llvm.org/D92327

This covers cases that are not folded away because the extent tensor type
becomes more concrete in the process.

Differential Revision: https://reviews.llvm.org/D98782

Add a feature to `EnumAttr` definition to generate
specialized Attribute class for the particular enumeration.

This class will inherit `StringAttr` or `IntegerAttr` and
will override `classof` and `getValue` methods.

With this class the enumeration predicate can be checked with simple
RTTI calls (`isa`, `dyn_cast`) and it will return the typed enumeration
directly instead of raw string/integer.

Based on the following discussion:
https://llvm.discourse.group/t/rfc-add-enum-attribute-decorator-class/2252

Reviewed By: rriddle

Differential Revision: https://reviews.llvm.org/D97836

'ForOpIterArgsFolder' can now remove iterator arguments (and corresponding
results) with no use.

Example:

```
%cst = constant 32 : i32

%0:2 = scf.for %arg1 = %lb to %ub step %step iter_args(%arg2 = %arg0, %arg3 = %cst)
  -> (i32, i32) {
  %1 = addu %arg2, %cst : i32
  scf.yield %1, %1 : i32, i32
}

use(%0#0)

```

%arg3 is not used in the block, and its corresponding result `%0#1` has no use,
thus remove the iter argument.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D98711

This revision extends the PDL Interpreter dialect to add support for variadic operands and results, with ranges of these values represented via the recently added !pdl.range type. To support this extension, three new operations have been added that closely match the single variant:
* pdl_interp.check_types : Compare a range of types with a known range.
* pdl_interp.create_types : Create a constant range of types.
* pdl_interp.get_operands : Get a range of operands from an operation.
* pdl_interp.get_results : Get a range of results from an operation.
* pdl_interp.switch_types : Switch on a range of types.

This revision handles adding support in the interpreter dialect and the conversion from PDL to PDLInterp. Support for variadic operands and results in the bytecode will be added in a followup revision.

Differential Revision: https://reviews.llvm.org/D95722

This revision extends the PDL dialect to add support for variadic operands and results, with ranges of these values represented via the recently added !pdl.range type. To support this extension, three new operations have been added that closely match the single variant:
* pdl.operands : Define a range of input operands.
* pdl.results : Extract a result group from an operation.
* pdl.types : Define a handle to a range of types.

Support for these in the pdl interpreter dialect and byte code will be added in followup revisions.

Differential Revision: https://reviews.llvm.org/D95721

This has a numerous amount of benefits, given the overly clunky nature of CreateNativeOp:
* Users can now call into arbitrary rewrite functions from inside of PDL, allowing for more natural interleaving of PDL/C++ and enabling for more of the pattern to be in PDL.
* Removes the need for an additional set of C++ functions/registry/etc. The new ApplyNativeRewriteOp will use the same PDLRewriteFunction as the existing RewriteOp. This reduces the API surface area exposed to users.

This revision also introduces a new PDLResultList class. This class is used to provide results of native rewrite functions back to PDL. We introduce a new class instead of using a SmallVector to simplify the work necessary for variadics, given that ranges will require some changes to the structure of PDLValue.

Differential Revision: https://reviews.llvm.org/D95720

Up until now, results have been represented as additional results to a pdl.operation. This is fairly clunky, as it mismatches the representation of the rest of the IR constructs(e.g. pdl.operand) and also isn't a viable representation for operations returned by pdl.create_native. This representation also creates much more difficult problems when factoring in support for variadic result groups, optional results, etc. To resolve some of these problems, and simplify adding support for variable length results, this revision extracts the representation for results out of pdl.operation in the form of a new `pdl.result` operation. This operation returns the result of an operation at a given index, e.g.:

```
%root = pdl.operation ...
%result = pdl.result 0 of %root
```

Differential Revision: https://reviews.llvm.org/D95719

Differential Revision: https://reviews.llvm.org/D97542

scf::ForOp: Fold away iterator arguments with no use and for which the corresponding input is yielded

Enhance 'ForOpIterArgsFolder' to remove unused iteration arguments in a
scf::ForOp. If the block argument corresponding to the given iterator has no
use and the yielded value equals the input, we fold it away.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D98503

The Intel Advanced Matrix Extensions (AMX) provides a tile matrix
multiply unit (TMUL), a tile control register (TILECFG), and eight
tile registers TMM0 through TMM7 (TILEDATA). This new MLIR dialect
provides a bridge between MLIR concepts like vectors and memrefs
and the lower level LLVM IR details of AMX.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D98470

The patch in question broke the build with shared libraries due to
missing dependencies, one of which would have been circular between
MLIRStandard and MLIRMemRef if added. Fix this by moving more code
around and swapping the dependency direction. MLIRMemRef now depends on
MLIRStandard, but MLIRStandard does _not_ depend on MLIRMemRef.
Arguably, this is the right direction anyway since numerous libraries
depend on MLIRStandard and don't necessarily need to depend on
MLIRMemref.

Other otable changes include:
- some EDSC code is moved inline to MemRef/EDSC/Intrinsics.h because it
  creates MemRef dialect operations;
- a utility function related to shape moved to BuiltinTypes.h/cpp
  because it only realtes to shaped types and not any particular dialect
  (standard dialect is erroneously believed to contain MemRefType);
- a Python test for the standard dialect is disabled completely because
  the ops it tests moved to the new MemRef dialect, but it is not
  exposed to Python bindings, and the change for that is non-trivial.

Create the memref dialect and move dialect-specific ops
from std dialect to this dialect.

Moved ops:
AllocOp -> MemRef_AllocOp
AllocaOp -> MemRef_AllocaOp
AssumeAlignmentOp -> MemRef_AssumeAlignmentOp
DeallocOp -> MemRef_DeallocOp
DimOp -> MemRef_DimOp
MemRefCastOp -> MemRef_CastOp
MemRefReinterpretCastOp -> MemRef_ReinterpretCastOp
GetGlobalMemRefOp -> MemRef_GetGlobalOp
GlobalMemRefOp -> MemRef_GlobalOp
LoadOp -> MemRef_LoadOp
PrefetchOp -> MemRef_PrefetchOp
ReshapeOp -> MemRef_ReshapeOp
StoreOp -> MemRef_StoreOp
SubViewOp -> MemRef_SubViewOp
TransposeOp -> MemRef_TransposeOp
TensorLoadOp -> MemRef_TensorLoadOp
TensorStoreOp -> MemRef_TensorStoreOp
TensorToMemRefOp -> MemRef_BufferCastOp
ViewOp -> MemRef_ViewOp

The roadmap to split the memref dialect from std is discussed here:
https://llvm.discourse.group/t/rfc-split-the-memref-dialect-from-std/2667

Differential Revision: https://reviews.llvm.org/D98041

Remove redundant operands and fold if only one left.

Differential Revision: https://reviews.llvm.org/D98402

This is a temporary work-around to get our all-annotations-all-flags
stress testing effort run clean. In the long run, we want to provide
efficient implementations of strided loads and stores though

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D98563

Functions used only in `assert` cause warnings in release mode

Reviewed By: mehdi_amini, dcaballe, ftynse

Differential Revision: https://reviews.llvm.org/D98476

This restricts the attributes to integers for constants of type
IndexType. So far an attribute like StringAttr as in

  %c1 = constant "" : index

is valid.

Reviewed By: mehdi_amini

Differential Revision: https://reviews.llvm.org/D98216

This patch introduces progressive lowering patterns for rewriting
vector.transfer_read/write to vector.load/store and vector.broadcast
in certain supported cases.

Reviewed By: dcaballe, nicolasvasilache

Differential Revision: https://reviews.llvm.org/D97822

OperationState is a low level API that is rarely indicated, the builder
API convenient wrapper is preferred when possible.

This patch adds support for vectorizing loops with 'iter_args' when those loops
are not a vector dimension. This allows vectorizing outer loops with an inner
'iter_args' loop (e.g., reductions). Vectorizing scenarios where 'iter_args'
loops are vector dimensions would require more work (e.g., analysis,
generating horizontal reduction, etc.) not included in this patch.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D97892

This patch replaces the root-terminal vectorization approach implemented in the
Affine vectorizer with a topological order approach that vectorizes all the
operations within the target loop nest. These are the most important changes
introduced by the new algorithm:
  * Removed tracking of root and terminal ops. Existing vectorization
    functionality is preserved and extended so that loop nests without
    root-terminal chains can be vectorized.
  * Vectorizing a loop nest now only requires a single topological traversal.
  * A new vector loop nest is incrementally built along the vectorization
    process. The original scalar loop is kept intact. No cloning guard is needed
    to recover the scalar loop if vectorization fails. This approach also
    simplifies the challenging task of replacing a loop operation amid the
    vectorization process without invalidating the analysis information that
    depends on the original loop.
  * Vectorization of specific operations has been implemented as independent,
    preparing them to be moved to a potential vectorization interface.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D97442

This allows for storage instances to store data that isn't uniqued in the context, or contain otherwise non-trivial logic, in the rare situations that they occur. Storage instances with trivial destructors will still have their destructor skipped. A consequence of this is that the storage instance definition must be visible from the place that registers the type.

Differential Revision: https://reviews.llvm.org/D98311

This patch fixes a heap-use-after-free introduced by the recent changes
in the vectorizer: https://reviews.llvm.org/rG95db7b4aeaad590f37720898e339a6d54313422f
The problem is due to the way candidate loops are visited. All candidate loops
are pattern-matched beforehand using the 'NestedMatch' utility. These matches may
intersect with each other so it may happen that we try to vectorize a loop that
was previously vectorized. The new vectorization algorithm replaces the original
loops that are vectorized with new loops and, therefore, any reference to the
original loops in the pre-computed matches becomes invalid.

This patch fixes the problem by classifying the candidate matches into buckets
before vectorization. Each bucket contains all the matches that intersect. The
vectorizer uses these buckets to make sure that we only vectorize *one* match from
each bucket, at most.

Differential Revision: https://reviews.llvm.org/D98382

Data layout information allows to answer questions about the size and alignment
properties of a type. It enables, among others, the generation of various
linear memory addressing schemes for containers of abstract types and deeper
reasoning about vectors. This introduces the subsystem for modeling data
layouts in MLIR.

The data layout subsystem is designed to scale to MLIR's open type and
operation system. At the top level, it consists of attribute interfaces that
can be implemented by concrete data layout specifications; type interfaces that
should be implemented by types subject to data layout; operation interfaces
that must be implemented by operations that can serve as data layout scopes
(e.g., modules); and dialect interfaces for data layout properties unrelated to
specific types. Built-in types are handled specially to decrease the overall
query cost.

A concrete default implementation of these interfaces is provided in the new
Target dialect. Defaults for built-in types that match the current behavior are
also provided.

Reviewed By: rriddle

Differential Revision: https://reviews.llvm.org/D97067

Support specifying the II and disabling pipelining.

Reviewed By: ftynse

Differential Revision: https://reviews.llvm.org/D98420