This class represents a rewrite pattern list that has been frozen, and thus immutable. This replaces the uses of OwningRewritePatternList in pattern driver related API, such as dialect conversion. When PDL becomes more prevalent, this API will allow for optimizing a set of patterns once without the need to do this per run of a pass.

Differential Revision: https://reviews.llvm.org/D89104

There are several pieces of pattern rewriting infra in IR/ that really shouldn't be there. This revision moves those pieces to a better location such that they are easier to evolve in the future(e.g. with PDL). More concretely this revision does the following:

* Create a Transforms/GreedyPatternRewriteDriver.h and move the apply*andFold methods there.
The definitions for these methods are already in Transforms/ so it doesn't make sense for the declarations to be in IR.

* Create a new lib/Rewrite library and move PatternApplicator there.
This new library will be focused on applying rewrites, and will also include compiling rewrites with PDL.

Differential Revision: https://reviews.llvm.org/D89103

The Pattern class was originally intended to be used for solely matching operations, but that use never materialized. All of the pattern infrastructure uses RewritePattern, and the infrastructure for pure matching(Matchers.h) is implemented inline. This means that this class isn't a useful abstraction at the moment, so this revision refactors it to solely encapsulate the "metadata" of a pattern. The metadata includes the various state describing a pattern; benefit, root operation, etc. The API on PatternApplicator is updated to now operate on `Pattern`s as nothing special from `RewritePattern` is necessary.

This refactoring is also necessary for the upcoming use of PDL patterns alongside C++ rewrite patterns.

Differential Revision: https://reviews.llvm.org/D86258

The conversion between PDL and the interpreter is split into several different parts.
** The Matcher:

The matching section of all incoming pdl.pattern operations is converted into a predicate tree and merged. Each pattern is first converted into an ordered list of predicates starting from the root operation. A predicate is composed of three distinct parts:
* Position
  - A position refers to a specific location on the input DAG, i.e. an
    existing MLIR entity being matched. These can be attributes, operands,
    operations, results, and types. Each position also defines a relation to
    its parent. For example, the operand `[0] -> 1` has a parent operation
    position `[0]` (the root).
* Question
  - A question refers to a query on a specific positional value. For
  example, an operation name question checks the name of an operation
  position.
* Answer
  - An answer is the expected result of a question. For example, when
  matching an operation with the name "foo.op". The question would be an
  operation name question, with an expected answer of "foo.op".

After the predicate lists have been created and ordered(based on occurrence of common predicates and other factors), they are formed into a tree of nodes that represent the branching flow of a pattern match. This structure allows for efficient construction and merging of the input patterns. There are currently only 4 simple nodes in the tree:
* ExitNode: Represents the termination of a match
* SuccessNode: Represents a successful match of a specific pattern
* BoolNode/SwitchNode: Branch to a specific child node based on the expected answer to a predicate question.

Once the matcher tree has been generated, this tree is walked to generate the corresponding interpreter operations.

 ** The Rewriter:
The rewriter portion of a pattern is generated in a very straightforward manor, similarly to lowerings in other dialects. Each PDL operation that may exist within a rewrite has a mapping into the interpreter dialect. The code for the rewriter is generated within a FuncOp, that is invoked by the interpreter on a successful pattern match. Referenced values defined in the matcher become inputs the generated rewriter function.

An example lowering is shown below:

```mlir
// The following high level PDL pattern:
pdl.pattern : benefit(1) {
  %resultType = pdl.type
  %inputOperand = pdl.input
  %root, %results = pdl.operation "foo.op"(%inputOperand) -> %resultType
  pdl.rewrite %root {
    pdl.replace %root with (%inputOperand)
  }
}

// is lowered to the following:
module {
  // The matcher function takes the root operation as an input.
  func @matcher(%arg0: !pdl.operation) {
    pdl_interp.check_operation_name of %arg0 is "foo.op" -> ^bb2, ^bb1
  ^bb1:
    pdl_interp.return
  ^bb2:
    pdl_interp.check_operand_count of %arg0 is 1 -> ^bb3, ^bb1
  ^bb3:
    pdl_interp.check_result_count of %arg0 is 1 -> ^bb4, ^bb1
  ^bb4:
    %0 = pdl_interp.get_operand 0 of %arg0
    pdl_interp.is_not_null %0 : !pdl.value -> ^bb5, ^bb1
  ^bb5:
    %1 = pdl_interp.get_result 0 of %arg0
    pdl_interp.is_not_null %1 : !pdl.value -> ^bb6, ^bb1
  ^bb6:
    // This operation corresponds to a successful pattern match.
    pdl_interp.record_match @rewriters::@rewriter(%0, %arg0 : !pdl.value, !pdl.operation) : benefit(1), loc([%arg0]), root("foo.op") -> ^bb1
  }
  module @rewriters {
    // The inputs to the rewriter from the matcher are passed as arguments.
    func @rewriter(%arg0: !pdl.value, %arg1: !pdl.operation) {
      pdl_interp.replace %arg1 with(%arg0)
      pdl_interp.return
    }
  }
}
```

Differential Revision: https://reviews.llvm.org/D84580

Adds support for
- Dropping unit dimension loops for indexed_generic ops.
- Folding consecutive folding (or expanding) reshapes when the result
  (or src) is a scalar.
- Fixes to indexed_generic -> generic fusion when zero-dim tensors are
  involved.

Differential Revision: https://reviews.llvm.org/D90118

The alignment attribute in the 'alloca' op treats the '0' value as 'unset'.
When parsing the custom form of the 'alloca' op, ignore the alignment attribute
with if its value is '0' instead of actually creating it and producing a
slightly different textually yet equivalent semantically form in the output.

Reviewed By: rriddle

Differential Revision: https://reviews.llvm.org/D90179

https://llvm.discourse.group/t/rfc-standard-memref-cast-ops/1454/15

Differential Revision: https://reviews.llvm.org/D90033

Based on discourse discussion, fix the doc string and remove examples with
wrong semantic. Also fix insert_map semantic by adding missing operand for
vector we are inserting into.

Differential Revision: https://reviews.llvm.org/D89563

This revision allows the fusion of the producer of input tensors in the consumer under a tiling transformation (which produces subtensors).
Many pieces are still missing (e.g. support init_tensors, better refactor LinalgStructuredOp interface support, try to merge implementations and reuse code) but this still allows getting started.

The greedy pass itself is just for testing purposes and will be extracted in a separate test pass.

Differential revision: https://reviews.llvm.org/D89491

This patch introduces a SPIR-V runner. The aim is to run a gpu
kernel on a CPU via GPU -> SPIRV -> LLVM conversions. This is a first
prototype, so more features will be added in due time.

- Overview
The runner follows similar flow as the other runners in-tree. However,
having converted the kernel to SPIR-V, we encode the bind attributes of
global variables that represent kernel arguments. Then SPIR-V module is
converted to LLVM. On the host side, we emulate passing the data to device
by creating in main module globals with the same symbolic name as in kernel
module. These global variables are later linked with ones from the nested
module. We copy data from kernel arguments to globals, call the kernel
function from nested module and then copy the data back.

- Current state
At the moment, the runner is capable of running 2 modules, nested one in
another. The kernel module must contain exactly one kernel function. Also,
the runner supports rank 1 integer memref types as arguments (to be scaled).

- Enhancement of JitRunner and ExecutionEngine
To translate nested modules to LLVM IR, JitRunner and ExecutionEngine were
altered to take an optional (default to `nullptr`) function reference that
is a custom LLVM IR module builder. This allows to customize LLVM IR module
creation from MLIR modules.

Reviewed By: ftynse, mravishankar

Differential Revision: https://reviews.llvm.org/D86108

This patch introduces a pass for running
`mlir-spirv-cpu-runner` - LowerHostCodeToLLVMPass.

This pass emulates `gpu.launch_func` call in LLVM dialect and lowers
the host module code to LLVM. It removes the `gpu.module`, creates a
sequence of global variables that are later linked to the varables
in the kernel module, as well as a series of copies to/from
them to emulate the memory transfer to/from the host or to/from the
device sides. It also converts the remaining Standard dialect into
LLVM dialect, emitting C wrappers.

Reviewed By: mravishankar

Differential Revision: https://reviews.llvm.org/D86112

This dependency was already existing indirectly, but is now more direct
since the registration relies on a inline function. This fixes the
link of the tools with BFD.

This has been deprecated for >1month now and removal was announced in:

https://llvm.discourse.group/t/rfc-revamp-dialect-registration/1559/11

Differential Revision: https://reviews.llvm.org/D86356

This reverts commit b22e2e4c.

Investigating broken builds

This has been deprecated for >1month now and removal was announced in:

https://llvm.discourse.group/t/rfc-revamp-dialect-registration/1559/11

Differential Revision: https://reviews.llvm.org/D86356

AsyncRuntime must be explicitly linked with LLVM pthreads

Reviewed By: mehdi_amini

Differential Revision: https://reviews.llvm.org/D89983

Add folder for the case where ExtractStridedSliceOp source comes from a chain
of InsertStridedSliceOp. Also add a folder for the trivial case where the
ExtractStridedSliceOp is a no-op.

Differential Revision: https://reviews.llvm.org/D89850

Differential Revision: https://reviews.llvm.org/D89853

I just found I needed this in an upcoming patch, and it seems generally
useful to have.

Differential Revision: https://reviews.llvm.org/D90000

Extract buffer alias analysis from buffer placement.

Differential Revision: https://reviews.llvm.org/D89902

This patch provides C API for MLIR affine expression.
- Implement C API for methods of AffineExpr class.
- Implement C API for methods of derived classes (AffineBinaryOpExpr, AffineDimExpr, AffineSymbolExpr, and AffineConstantExpr).

Differential Revision: https://reviews.llvm.org/D89856

Added optimization pass to convert heap-based allocs to stack-based allocas in
buffer placement. Added the corresponding test file.

Differential Revision: https://reviews.llvm.org/D89688

[mlir] Fix exiting OpPatternRewriteDriver::simplifyLocally after first iteration that didn't change the op.

Before this change, we would run `maxIterations` if the first iteration changed the op.
After this change, we exit the loop as soon as an iteration hasn't changed the op.
Assuming that we have reached a fixed point when an iteration doesn't change the op, this doesn't affect correctness.

Reviewed By: rriddle

Differential Revision: https://reviews.llvm.org/D89981

This reverts commit 4986d5ea with
proper patches to CMakeLists.txt:

- Add MLIRAsync as a dependency to MLIRAsyncToLLVM
- Add Coroutines as a dependency to MLIRExecutionEngine

This reverts commit a8b0ae3b
and commit f8fcff5a.

The build with SHARED_LIBRARY=ON is broken.

pthreads is not enabled for all builds by default

Reviewed By: jpienaar

Differential Revision: https://reviews.llvm.org/D89967

Lower from Async dialect to LLVM by converting async regions attached to `async.execute` operations into LLVM coroutines (https://llvm.org/docs/Coroutines.html):
1. Outline all async regions to functions
2. Add LLVM coro intrinsics to mark coroutine begin/end
3. Use MLIR conversion framework to convert all remaining async types and ops to LLVM + Async runtime function calls

All `async.await` operations inside async regions converted to coroutine suspension points. Await operation outside of a coroutine converted to the blocking wait operations.

Implement simple runtime to support concurrent execution of coroutines.

Reviewed By: herhut

Differential Revision: https://reviews.llvm.org/D89292

Reuse most code for printing/parsing/verification from SubViewOp.

https://llvm.discourse.group/t/rfc-standard-memref-cast-ops/1454/15

Differential Revision: https://https://reviews.llvm.org/D89720

https://llvm.discourse.group/t/rfc-standard-memref-cast-ops/1454/15

Differential Revision: https://reviews.llvm.org/D89784

[mlir] Simplify DDR matching patterns with equal operands for operators where it's applicable. Added documentation.

This https://reviews.llvm.org/D89254 diff introduced implicit matching between same name operands.

Differential Revision: https://reviews.llvm.org/D89598

Forward missing attributes when creating the new transfer op otherwise the
builder would use default values.

Differential Revision: https://reviews.llvm.org/D89907

MLIRTransforms is needed to provide BufferizeTypeConverter
definitions.

* Adds a new MlirOpPrintingFlags type and supporting accessors.
* Adds a new mlirOperationPrintWithFlags function.
* Adds a full featured python Operation.print method with all options and the ability to print directly to files/stdout in text or binary.
* Adds an Operation.get_asm which delegates to print and returns a str or bytes.
* Reworks Operation.__str__ to be based on get_asm.

Differential Revision: https://reviews.llvm.org/D89848

A "structural" type conversion is one where the underlying ops are
completely agnostic to the actual types involved and simply need to update
their types. An example of this is shape.assuming -- the shape.assuming op
and the corresponding shape.assuming_yield op need to update their types
accordingly to the TypeConverter, but otherwise don't care what type
conversions are happening.

Also, the previous conversion code would not correctly materialize
conversions for the shape.assuming_yield op. This should have caused a
verification failure, but shape.assuming's verifier wasn't calling
RegionBranchOpInterface::verifyTypes (which for reasons can't be called
automatically as part of the trait verification, and requires being
called manually). This patch also adds that verification.

Differential Revision: https://reviews.llvm.org/D89833

A "structural" type conversion is one where the underlying ops are
completely agnostic to the actual types involved and simply need to update
their types. An example of this is scf.if -- the scf.if op and the
corresponding scf.yield ops need to update their types accordingly to the
TypeConverter, but otherwise don't care what type conversions are happening.

To test the structural type conversions, it is convenient to define a
bufferize pass for a dialect, which exercises them nicely.

Differential Revision: https://reviews.llvm.org/D89757

Reviewed By: herhut

Differential Revision: https://reviews.llvm.org/D89686

Reviewed By: herhut

Differential Revision: https://reviews.llvm.org/D89262

Docstrings for `__str__` method in many classes was recycling the constant
string defined for `Type`, without being types themselves. Use proper
docstrings instead. Since they are succint, use string literals instead of
top-level constants to avoid further mistakes.

Differential Revision: https://reviews.llvm.org/D89780