This will ensure that:
- The `field` of a class can have an initial value
- The `field` op is emitted correctly
- The `getfield` op is emitted correctly
This PR uses `val.getDefiningOp<OpTy>()` to replace `dyn_cast<OpTy>(val.getDefiningOp())` , `dyn_cast_or_null<OpTy>(val.getDefiningOp())` and `dyn_cast_if_present<OpTy>(val.getDefiningOp())`.
Goal: Enable using C++ classes to AOT compile models for MLGO.
This commit introduces a transformation pass that converts standalone
`emitc.func` operations into `emitc.class `structures to support
class-based C++ code generation for MLGO.
Transformation details:
- Wrap `emitc.func @func_name` into `emitc.class @Myfunc_nameClass`
- Converts function arguments to class fields with preserved attributes
- Transforms function body into an `execute()` method with no arguments
- Replaces argument references with `get_field` operations
Before: emitc.func @Model(%arg0, %arg1, %arg2) with direct argument
access
After: emitc.class with fields and execute() method using get_field
operations
This enables generating C++ classes that can be instantiated and
executed as self-contained model objects for AOT compilation workflows.
By defining `CExpressionInterface`, we move the side effect detection
logic from `emitc.expression` into the individual operations
implementing the interface allowing operations to gradually tune the
side effect.
It also allows checking for side effects each operation individually.
This commit updates the internal `ConversionValueMapping` data structure
in the dialect conversion driver to support 1:N replacements. This is
the last major commit for adding 1:N support to the dialect conversion
driver.
Since #116470, the infrastructure already supports 1:N replacements. But
the `ConversionValueMapping` still stored 1:1 value mappings. To that
end, the driver inserted temporary argument materializations (converting
N SSA values into 1 value). This is no longer the case. Argument
materializations are now entirely gone. (They will be deleted from the
type converter after some time, when we delete the old 1:N dialect
conversion driver.)
Note for LLVM integration: Replace all occurrences of
`addArgumentMaterialization` (except for 1:N dialect conversion passes)
with `addSourceMaterialization`.
---------
Co-authored-by: Markus Böck <markus.boeck02@gmail.com>
The greedy rewriter is used in many different flows and it has a lot of
convenience (work list management, debugging actions, tracing, etc). But
it combines two kinds of greedy behavior 1) how ops are matched, 2)
folding wherever it can.
These are independent forms of greedy and leads to inefficiency. E.g.,
cases where one need to create different phases in lowering and is
required to applying patterns in specific order split across different
passes. Using the driver one ends up needlessly retrying folding/having
multiple rounds of folding attempts, where one final run would have
sufficed.
Of course folks can locally avoid this behavior by just building their
own, but this is also a common requested feature that folks keep on
working around locally in suboptimal ways.
For downstream users, there should be no behavioral change. Updating
from the deprecated should just be a find and replace (e.g., `find ./
-type f -exec sed -i
's|applyPatternsAndFoldGreedily|applyPatternsGreedily|g' {} \;` variety)
as the API arguments hasn't changed between the two.
This commit simplifies the result type of materialization functions.
Previously: `std::optional<Value>`
Now: `Value`
The previous implementation allowed 3 possible return values:
- Non-null value: The materialization function produced a valid
materialization.
- `std::nullopt`: The materialization function failed, but another
materialization can be attempted.
- `Value()`: The materialization failed and so should the dialect
conversion. (Previously: Dialect conversion can roll back.)
This commit removes the last variant. It is not particularly useful
because the dialect conversion will fail anyway if all other
materialization functions produced `std::nullopt`.
Furthermore, in contrast to type conversions, at least one
materialization callback is expected to succeed. In case of a failing
type conversion, the current dialect conversion can roll back and try a
different pattern. This also used to be the case for materializations,
but that functionality was removed with #107109: failed materializations
can no longer trigger a rollback. (They can just make the entire dialect
conversion fail without rollback.) With this in mind, it is even less
useful to have an additional error state for materialization functions.
This commit is in preparation of merging the 1:1 and 1:N type
converters. Target materializations will have to return multiple values
instead of a single one. With this commit, we can keep the API simple:
`SmallVector<Value>` instead of `std::optional<SmallVector<Value>>`.
Note for LLVM integration: All 1:1 materializations should return
`Value` instead of `std::optional<Value>`. Instead of `std::nullopt`
return `Value()`.
This commit adds `emitc.size_t`, `emitc.ssize_t` and `emitc.ptrdiff_t`
types to the EmitC dialect. These are used to map `index` types to C/C++
types with an explicit signedness, and are emitted in C/C++ as `size_t`,
`ssize_t` and `ptrdiff_t`.
An `emitc.expression` can only yield a single result, but some
operations which have the `CExpression` trait can have multiple results,
which can result in a crash when applying the `fold-expressions` pass.
This change adds a check for the single-result condition and a simple
test.
When operations are modified in-place, the rewriter must be notified.
This commit fixes `mlir/test/Dialect/EmitC/transforms.mlir` when running
with `MLIR_ENABLE_EXPENSIVE_PATTERN_API_CHECKS` enabled.
Add an emitc.expression operation that models C expressions, and provide
transforms to form and fold expressions. The translator emits the body
of
emitc.expression ops as a single C expression.
This expression is emitted by default as the RHS of an EmitC SSA value,
but if
possible, expressions with a single use that is not another expression
are
instead inlined. Specific expression's inlining can be fine tuned by
lowering
passes and transforms.
