Arithmetic constants for vector types can be constructed from objects
implementing Python buffer protocol such as `array.array`. Note that
until Python 3.12, there is no typing support for buffer protocol
implementers, so the annotations use array explicitly.
Reverts llvm/llvm-project#84103
Arithmetic constants for vector types can be constructed from objects
implementing Python buffer protocol such as `array.array`. Note that
until Python 3.12, there is no typing support for buffer protocol
implementers, so the annotations use array explicitly.
From https://reviews.llvm.org/D153245
This adds support for native PDL (and PDLL) C++ constraints to return
results.
This is useful for situations where a pattern checks for certain
constraints of multiple interdependent attributes and computes a new
attribute value based on them. Currently, for such an example it is
required to escape to C++ during matching to perform the check and after
a successful match again escape to native C++ to perform the computation
during the rewriting part of the pattern. With this work we can do the
computation in C++ during matching and use the result in the rewriting
part of the pattern. Effectively this enables a choice in the trade-off
of memory consumption during matching vs recomputation of values.
This is an example of a situation where this is useful: We have two
operations with certain attributes that have interdependent constraints.
For instance `attr_foo: one_of [0, 2, 4, 8], attr_bar: one_of [0, 2, 4,
8]` and `attr_foo == attr_bar`. The pattern should only match if all
conditions are true. The new operation should be created with a new
attribute which is computed from the two matched attributes e.g.
`attr_baz = attr_foo * attr_bar`. For the check we already escape to
native C++ and have all values at hand so it makes sense to directly
compute the new attribute value as well:
```
Constraint checkAndCompute(attr0: Attr, attr1: Attr) -> Attr;
Pattern example with benefit(1) {
let foo = op<test.foo>() {attr = attr_foo : Attr};
let bar = op<test.bar>(foo) {attr = attr_bar : Attr};
let attr_baz = checkAndCompute(attr_foo, attr_bar);
rewrite bar with {
let baz = op<test.baz> {attr=attr_baz};
replace bar with baz;
};
}
```
To achieve this the following notable changes were necessary:
PDLL:
- Remove check in PDLL parser that prevented native constraints from
returning results
PDL:
- Change PDL definition of pdl.apply_native_constraint to allow variadic
results
PDL_interp:
- Change PDL_interp definition of pdl_interp.apply_constraint to allow
variadic results
PDLToPDLInterp Pass:
The input to the pass is an arbitrary number of PDL patterns. The pass
collects the predicates that are required to match all of the pdl
patterns and establishes an ordering that allows creation of a single
efficient matcher function to match all of them. Values that are matched
and possibly used in the rewriting part of a pattern are represented as
positions. This allows fusion and thus reusing a single position for
multiple matching patterns. Accordingly, we introduce
ConstraintPosition, which records the type and index of the result of
the constraint. The problem is for the corresponding value to be used in
the rewriting part of a pattern it has to be an input to the
pdl_interp.record_match operation, which is generated early during the
pass such that its surrounding block can be referred to by branching
operations. In consequence the value has to be materialized after the
original pdl.apply_native_constraint has been deleted but before we get
the chance to generate the corresponding pdl_interp.apply_constraint
operation. We solve this by emitting a placeholder value when a
ConstraintPosition is evaluated. These placeholder values (due to fusion
there may be multiple for one constraint result) are replaced later when
the actual pdl_interp.apply_constraint operation is created.
Changes since the phabricator review:
- Addressed all comments
- In particular, removed registerConstraintFunctionWithResults and
instead changed registerConstraintFunction so that contraint functions
always have results (empty by default)
- Thus we don't need to reuse `rewriteFunctions` to store constraint
functions with results anymore, and can instead use
`constraintFunctions`
- Perform a stable sort of ConstraintQuestion, so that
ConstraintQuestion appear before other ConstraintQuestion that use their
results.
- Don't create placeholders for pdl_interp::ApplyConstraintOp. Instead
generate the `pdl_interp::ApplyConstraintOp` before generating the
successor block.
- Fixed a test failure in the pdl python bindings
Original code by @martin-luecke
Co-authored-by: martin-luecke <martinpaul.luecke@amd.com>
_SubClassValueT is only useful when it is has >1 usage in a signature.
This was not true for the signatures produced by tblgen.
For example
def call(result, callee, operands_, *, loc=None, ip=None) ->
_SubClassValueT:
...
here a type checker does not have enough information to infer a type
argument for _SubClassValueT, and thus effectively treats it as Any.
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
Currently, a method exists to get the count of the operation objects
which are still alive. This helps for sanity checking, but isn't
terribly useful for debugging. This new method returns the actual
operation objects which are still alive.
This allows Python code like the following:
```
gc.collect()
live_ops = ir.Context.current._get_live_operation_objects()
for op in live_ops:
print(f"Warning: {op} is still live. Referrers:")
for referrer in gc.get_referrers(op)[0]:
print(f" {referrer}")
```
Following the discussion in
https://discourse.llvm.org/t/symboltable-and-symbol-parent-child-relationship/75446,
we should enforce that a symbol's immediate parent is a symbol table.
I changed some tests to pass the verification. In most cases, we can
wrap the func with a module, change the func to another op with regions
i.e. scf.if, or change the expected error message.
---------
Co-authored-by: Mehdi Amini <joker.eph@gmail.com>
1. C++ enum is set through enum class LevelType : uint_64.
2. C enum is set through typedef uint_64 level_type. It is due to the
limitations in Windows build: setting enum width to ui64 is not
supported in C.
The current implementation of `nvvm.wgmma.mma_async` Op deduces the data
type of the output matrix from the data type of struct member, which can be
non-intuitive, especially in cases where types like `2xf16` are packed
into `i32`.
This PR addresses this issue by improving the Op to include an explicit
data type for the output matrix.
The modified Op now includes an explicit data type for Matrix-D (<f16>),
and looks as follows:
```
%result = llvm.mlir.undef : !llvm.struct<(struct<(i32, i32, ...
nvvm.wgmma.mma_async
%descA, %descB, %result,
#nvvm.shape<m = 64, n = 32, k = 16>,
D [<f16>, #nvvm.wgmma_scale_out<zero>],
A [<f16>, #nvvm.wgmma_scale_in<neg>, <col>],
B [<f16>, #nvvm.wgmma_scale_in<neg>, <col>]
```
Add overflow flags support to the following ops:
* `arith.addi`
* `arith.subi`
* `arith.muli`
Example of new syntax:
```
%res = arith.addi %arg1, %arg2 overflow<nsw> : i64
```
Similar to existing LLVM dialect syntax
```
%res = llvm.add %arg1, %arg2 overflow<nsw> : i64
```
Tablegen canonicalization patterns updated to always drop flags, proper
support with tests will be added later.
Updated LLVMIR translation as part of this commit as it currenly written
in a way that it will crash when new attributes added to arith ops
otherwise.
Also lower `arith` overflow flags to corresponding SPIR-V op decorations
Discussion
https://discourse.llvm.org/t/rfc-integer-overflow-flags-support-in-arith-dialect/76025
This effectively rolls forward #77211, #77700 and #77714 while adding a
test to ensure the Python usage is not broken. More follow up needed but
unrelated to the core change here. The changes here are minimal and just
correspond to "textual namespacing" ODS side, no C++ or Python changes
were needed.
---------
---------
Co-authored-by: Ivan Butygin <ivan.butygin@gmail.com>, Yi Wu <yi.wu2@arm.com>
In addition to the existing `OpHandle` which provides an abstraction to
emit transform ops targeting operations this introduces a similar
concept for _values_ and _parameters_ in form of `ValueHandle` and
`ParamHandle`.
New core transform abstractions:
- `constant_param`
- `OpHandle.get_result`
- `OpHandle.print`
- `ValueHandle.get_defining_op`
Add overflow flags support to the following ops:
* `arith.addi`
* `arith.subi`
* `arith.muli`
Example of new syntax:
```
%res = arith.addi %arg1, %arg2 overflow<nsw> : i64
```
Similar to existing LLVM dialect syntax
```
%res = llvm.add %arg1, %arg2 overflow<nsw> : i64
```
Tablegen canonicalization patterns updated to always drop flags, proper
support with tests will be added later.
Updated LLVMIR translation as part of this commit as it currenly written
in a way that it will crash when new attributes added to arith ops
otherwise.
Discussion
https://discourse.llvm.org/t/rfc-integer-overflow-flags-support-in-arith-dialect/76025
---------
Co-authored-by: Yi Wu <yi.wu2@arm.com>
This reverts commit bbc2976868.
This change seems to be at odds with the non-owning part semantics of
MlirOperation in C API. Since downstream clients can only take and
return MlirOperation, it does not sound correct to force all returns of
MlirOperation transfer ownership. Specifically, this makes it impossible
for downstreams to implement IR-traversing functions that, e.g., look at
neighbors of an operation.
The following patch triggers the exception, and there does not seem to
be an alternative way for a downstream binding writer to express this:
```
diff --git a/mlir/lib/Bindings/Python/IRCore.cpp b/mlir/lib/Bindings/Python/IRCore.cpp
index 39757dfad5be..2ce640674245 100644
--- a/mlir/lib/Bindings/Python/IRCore.cpp
+++ b/mlir/lib/Bindings/Python/IRCore.cpp
@@ -3071,6 +3071,11 @@ void mlir::python::populateIRCore(py::module &m) {
py::arg("successors") = py::none(), py::arg("regions") = 0,
py::arg("loc") = py::none(), py::arg("ip") = py::none(),
py::arg("infer_type") = false, kOperationCreateDocstring)
+ .def("_get_first_in_block", [](PyOperation &self) -> MlirOperation {
+ MlirBlock block = mlirOperationGetBlock(self.get());
+ MlirOperation first = mlirBlockGetFirstOperation(block);
+ return first;
+ })
.def_static(
"parse",
[](const std::string &sourceStr, const std::string &sourceName,
diff --git a/mlir/test/python/ir/operation.py b/mlir/test/python/ir/operation.py
index f59b1a26ba48..6b12b8da5c24 100644
--- a/mlir/test/python/ir/operation.py
+++ b/mlir/test/python/ir/operation.py
@@ -24,6 +24,25 @@ def expect_index_error(callback):
except IndexError:
pass
+@run
+def testCustomBind():
+ ctx = Context()
+ ctx.allow_unregistered_dialects = True
+ module = Module.parse(
+ r"""
+ func.func @f1(%arg0: i32) -> i32 {
+ %1 = "custom.addi"(%arg0, %arg0) : (i32, i32) -> i32
+ return %1 : i32
+ }
+ """,
+ ctx,
+ )
+ add = module.body.operations[0].regions[0].blocks[0].operations[0]
+ op = add.operation
+ # This will get a reference to itself.
+ f1 = op._get_first_in_block()
+
+
# Verify iterator based traversal of the op/region/block hierarchy.
# CHECK-LABEL: TEST: testTraverseOpRegionBlockIterators
```
This fixes a longstanding bug in the `Context._CAPICreate` method
whereby it was not taking ownership of the PyMlirContext wrapper when
casting to a Python object. The result was minimally that all such
contexts transferred in that way would leak. In addition, counter to the
documentation for the `_CAPICreate` helper (see
`mlir-c/Bindings/Python/Interop.h`) and the `forContext` /
`forOperation` methods, we were silently upgrading any unknown
context/operation pointer to steal-ownership semantics. This is
dangerous and was causing some subtle bugs downstream where this
facility is getting the most use.
This patch corrects the semantics and will only do an ownership transfer
for `_CAPICreate`, and it will further require that it is an ownership
transfer (if already transferred, it was just silently succeeding).
Removing the mis-aligned behavior made it clear where the downstream was
doing the wrong thing.
It also adds some `_testing_` functions to create unowned context and
operation capsules so that this can be fully tested upstream, reworking
the tests to verify the behavior.
In some torture testing downstream, I was not able to trigger any memory
corruption with the newly enforced semantics. When getting it wrong, a
regular exception is raised.
The functionality to `-print-ir-after-all` was added in
caa159f044.
This PR adds a test and, with that, some documentation.
---------
Co-authored-by: Maksim Levental <maksim.levental@gmail.com>
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.
Enables reusing the AsmState when printing from Python. Also moves the
fileObject and binary to the end (pybind11::object was resulting in the
overload not working unless `state=` was specified).
---------
Co-authored-by: Maksim Levental <maksim.levental@gmail.com>
