Being able to add custom dialects is one of the big missing pieces of
the C API. This change should make it achievable via IRDL. Hopefully
this should open custom dialect definition to non-C++ users of MLIR.
1. Explicit value means the non-zero value in a sparse tensor. If
explicitVal is set, then all the non-zero values in the tensor have the
same explicit value. The default value Attribute() indicates that it is
not set.
2. Implicit value means the "zero" value in a sparse tensor. If
implicitVal is set, then the "zero" value in the tensor is equal to the
implicit value. For now, we only support `0` as the implicit value but
it could be extended in the future. The default value Attribute()
indicates that the implicit value is `0` (same type as the tensor
element type).
Example:
```
#CSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : dense, d1 : compressed),
posWidth = 64,
crdWidth = 64,
explicitVal = 1 : i64,
implicitVal = 0 : i64
}>
```
Note: this PR tests that implicitVal could be set to other values as
well. The following PR will add verifier and reject any value that's not
zero for implicitVal.
This commit adds `walk` method to PyOperationBase that uses a python
object as a callback, e.g. `op.walk(callback)`. Currently callback must
return a walk result explicitly.
We(SiFive) have implemented walk method with python in our internal
python tool for a while. However the overhead of python is expensive and
it didn't scale well for large MLIR files. Just replacing walk with this
version reduced the entire execution time of the tool by 30~40% and
there are a few configs that the tool takes several hours to finish so
this commit significantly improves tool performance.
Transform interfaces are implemented, direction or via extensions, in
libraries belonging to multiple other dialects. Those dialects don't
need to depend on the non-interface part of the transform dialect, which
includes the growing number of ops and transitive dependency footprint.
Split out the interfaces into a separate library. This in turn requires
flipping the dependency from the interface on the dialect that has crept
in because both co-existed in one library. The interface shouldn't
depend on the transform dialect either.
As a consequence of splitting, the capability of the interpreter to
automatically walk the payload IR to identify payload ops of a certain
kind based on the type used for the entry point symbol argument is
disabled. This is a good move by itself as it simplifies the interpreter
logic. This functionality can be trivially replaced by a
`transform.structured.match` operation.
This commit extends the DIDerivedTypeAttr with the `extraData` field.
For now, the type of it is limited to be a `DINodeAttr`, as extending
the debug metadata handling to support arbitrary metadata nodes does not
seem to be necessary so far.
Following the discussion from [this
thread](https://discourse.llvm.org/t/handling-cyclic-dependencies-in-debug-info/67526/11),
this PR adds support for recursive DITypes.
This PR adds:
1. DIRecursiveTypeAttrInterface: An interface that DITypeAttrs can
implement to indicate that it supports recursion. See full description
in code.
2. Importer & exporter support (The only DITypeAttr that implements the
interface is DICompositeTypeAttr, so the exporter is only implemented
for composites too. There will be two methods that each llvm DI type
that supports mutation needs to implement since there's nothing
general).
---------
Co-authored-by: Tobias Gysi <tobias.gysi@nextsilicon.com>
The base class llvm::ThreadPoolInterface will be renamed
llvm::ThreadPool in a subsequent commit.
This is a breaking change: clients who use to create a ThreadPool must
now create a DefaultThreadPool instead.
Expose the API for constructing and inspecting StructTypes from the LLVM
dialect. Separate constructor methods are used instead of overloads for
better readability, similarly to IntegerType.
…ct LevelType from LevelFormat and properties instead.
**Rationale**
We used to explicitly declare every possible combination between
`LevelFormat` and `LevelProperties`, and it now becomes difficult to
scale as more properties/level formats are going to be introduced.
1. Add python test for n out of m
2. Add more methods for python binding
3. Add verification for n:m and invalid encoding tests
4. Add e2e test for n:m
Previous PRs for n:m #80501#79935
When properties are not enabled in an operation, inherent attributes are
stored in the common dictionary with discardable attributes. However,
`getDiscardableAttrs` and `getDiscardableAttrDictionary` were returning
the entire dictionary, making the caller mistakenly believe that all
inherent attributes are discardable. Fix this by filtering out
attributes whose names are registered with the operation, i.e., inherent
attributes. This requires an API change so `getDiscardableAttrs` returns
a filter range.
This adds Python abstractions for the different handle types of the
transform dialect
The abstractions allow for straightforward chaining of transforms by
calling their member functions.
As an initial PR for this infrastructure, only a single transform is
included: `transform.structured.match`.
With a future `tile` transform abstraction an example of the usage is:
```Python
def script(module: OpHandle):
module.match_ops(MatchInterfaceEnum.TilingInterface).tile(tile_sizes=[32,32])
```
to generate the following IR:
```mlir
%0 = transform.structured.match interface{TilingInterface} in %arg0
%tiled_op, %loops = transform.structured.tile_using_for %0 [32, 32]
```
These abstractions are intended to enhance the usability and flexibility
of the transform dialect by providing an accessible interface that
allows for easy assembly of complex transformation chains.
The "Dim" prefix is a legacy left-over that no longer makes sense, since
we have a very strict "Dimension" vs. "Level" definition for sparse
tensor types and their storage.
The scalable dimension functionality was added to the vector type after
the bindings for it were defined, without the bindings being ever
updated. Fix that.
Enable passing in MlirAsmState optionally (allow for passing in null) to
allow using the more efficient print calling API. The existing print
behavior results in a new AsmState is implicitly created by walking the
parent op and renumbering values. This makes the cost more explicit and
avoidable (by reusing an AsmState).
This commit changes the LLVM dialect's CAPI pointer getters to drop
support for typed pointers. Typed pointers are deprecated and should no
longer be generated.
Fixes https://github.com/llvm/llvm-project/issues/69730 (also see
https://reviews.llvm.org/D155543).
There are two things outstanding (why I didn't land before):
1. add some C API tests for `mlirOperationWalk`;
2. potentially refactor how the invalidation in `run` works; the first
version of the code looked like this:
```cpp
if (invalidateOps) {
auto *context = op.getOperation().getContext().get();
MlirOperationWalkCallback invalidatingCallback =
[](MlirOperation op, void *userData) {
PyMlirContext *context =
static_cast<PyMlirContext *>(userData);
context->setOperationInvalid(op);
};
auto numRegions =
mlirOperationGetNumRegions(op.getOperation().get());
for (int i = 0; i < numRegions; ++i) {
MlirRegion region =
mlirOperationGetRegion(op.getOperation().get(), i);
for (MlirBlock block = mlirRegionGetFirstBlock(region);
!mlirBlockIsNull(block);
block = mlirBlockGetNextInRegion(block))
for (MlirOperation childOp =
mlirBlockGetFirstOperation(block);
!mlirOperationIsNull(childOp);
childOp = mlirOperationGetNextInBlock(childOp))
mlirOperationWalk(childOp, invalidatingCallback, context,
MlirWalkPostOrder);
}
}
```
This is verbose and ugly but it has the important benefit of not
executing `mlirOperationEqual(rootOp->get(), op)` for every op
underneath the root op.
Supposing there's no desire for the slightly more efficient but highly
convoluted approach, I can land this "posthaste".
But, since we have eyes on this now, any suggestions or approaches (or
needs/concerns) are welcome.
Updates:
1. Infer lvlToDim from dimToLvl
2. Add more tests for block sparsity
3. Finish TODOs related to lvlToDim, including adding lvlToDim to python
binding
Verification of lvlToDim that user provides will be implemented in the
next PR.
This is a follow-up to 8c2bff1ab9 which lazy-initialized the
diagnostic and removed the need to dynamically abandon() an
InFlightDiagnostic. This further simplifies the code to not needed to
return a reference to an InFlightDiagnostic and instead eagerly emit
errors.
Also use `emitError` as name instead of `getDiag` which seems more
explicit and in-line with the common usage.
This is part of the transition toward properly splitting the two groups.
This only introduces new C APIs, the Python bindings are unaffected. No
API is removed.
Note the new surface syntax allows for defining a dimToLvl and lvlToDim
map at once (where usually the latter can be inferred from the former,
but not always). This revision adds storage for the latter, together
with some intial boilerplate. The actual support (inference, validation,
printing, etc.) is still TBD of course.
Enable usage where capturing AsmState is good (e.g., avoiding creating AsmState over and over again when walking IR and printing).
This also only changes one C API to verify plumbing. But using the AsmState makes the cost more explicit than the flags interface (which hides the traversals and construction here) and also enables a more efficient usage C side.
Only construction and type casting are implemented. The method to create
is explicitly named "unsafe" and the documentation calls out what the
caller is responsible for. There really isn't a better way to do this
and retain the power-user feature this represents.
