This PR implements "automatic" location inference in the bindings. The
way it works is it walks the frame stack collecting source locations
(Python captures these in the frame itself). It is inspired by JAX's
[implementation](523ddcfbca/jax/_src/interpreters/mlir.py (L462))
but moves the frame stack traversal into the bindings for better
performance.
The system supports registering "included" and "excluded" filenames;
frames originating from functions in included filenames **will not** be
filtered and frames originating from functions in excluded filenames
**will** be filtered (in that order). This allows excluding all the
generated `*_ops_gen.py` files.
The system is also "toggleable" and off by default to save people who
have their own systems (such as JAX) from the added cost.
Note, the system stores the entire stacktrace (subject to
`locTracebackFramesLimit`) in the `Location` using specifically a
`CallSiteLoc`. This can be useful for profiling tools (flamegraphs
etc.).
Shoutout to the folks at JAX for coming up with a good system.
---------
Co-authored-by: Jacques Pienaar <jpienaar@google.com>
- Introduces a `large_resource_limit` parameter across Python bindings,
enabling the eliding of resource strings exceeding a specified character
limit during IR printing.
- To maintain backward compatibilty, when using `operation.print()` API,
if `large_resource_limit` is None and the `large_elements_limit` is set,
the later will be used to elide the resource string as well. This change
was introduced by https://github.com/llvm/llvm-project/pull/125738.
- For printing using pass manager, the `large_resource_limit` and
`large_elements_limit` are completely independent of each other.
In some projects like JAX ir.Context are used with disabled multi-threading to avoid
caching multiple threading pools:
623865fe95/jax/_src/interpreters/mlir.py (L606-L611)
However, when context has enabled multithreading it also uses locks on
the StorageUniquers and this can be helpful to avoid data races in the
multi-threaded execution (for example with free-threaded cpython,
https://github.com/jax-ml/jax/issues/26272).
With this PR user can enable the multi-threading: 1) enables additional
locking and 2) set a shared threading pool such that cached contexts can
have one global pool.
This logic is in the critical path for constructing an operation from
Python. It is faster to compute this in C++ than it is in Python, and it
is a minor change to do this.
This change also alters the API contract of
_ods_common.get_op_results_or_values to avoid calling
get_op_result_or_value on each element of a sequence, since the C++ code
will now do this.
Most of the diff here is simply reordering the code in IRCore.cpp.
Currently we make two memory allocations for each PyOperation: a Python
object, and the PyOperation class itself. With some care we can allocate
the PyOperation inline inside the Python object, saving us a malloc()
call per object and perhaps improving cache locality.
Previously ODS-generated Python operations had code like this:
```
super().__init__(self.build_generic(attributes=attributes, operands=operands, successors=_ods_successors, regions=regions, loc=loc, ip=ip))
```
we change it to:
```
super().__init__(self.OPERATION_NAME, self._ODS_REGIONS, self._ODS_OPERAND_SEGMENTS, self._ODS_RESULT_SEGMENTS, attributes=attributes, operands=operands, successors=_ods_successors, regions=regions, loc=loc, ip=ip)
```
This:
a) avoids an extra call dispatch (to `build_generic`), and
b) passes the class attributes directly to the constructor. Benchmarks
show that it is faster to pass these as arguments rather than having the
C++ code look up attributes on the class.
This PR improves the timing of the following benchmark on my workstation
from 5.3s to 4.5s:
```
def main(_):
with ir.Context(), ir.Location.unknown():
typ = ir.IntegerType.get_signless(32)
m = ir.Module.create()
with ir.InsertionPoint(m.body):
start = time.time()
for i in range(1000000):
arith.ConstantOp(typ, i)
end = time.time()
print(f"time: {end - start}")
```
Since this change adds an additional overload to the constructor and
does not alter any existing behaviors, it should be backwards
compatible.
In JAX, I observed a race between two PyOperation destructors from
different threads updating the same `liveOperations` map, despite not
intentionally sharing the context between different threads. Since I
don't think we can be completely sure when GC happens and on which
thread, it seems safest simply to add locking here.
We may also want to explicitly support sharing a context between threads
in the future, which would require this change or something similar.
This is a companion to #118583, although it can be landed independently
because since #117922 dialects do not have to use the same Python
binding framework as the Python core code.
This PR ports all of the in-tree dialect and pass extensions to
nanobind, with the exception of those that remain for testing pybind11
support.
This PR also:
* removes CollectDiagnosticsToStringScope from NanobindAdaptors.h. This
was overlooked in a previous PR and it is duplicated in Diagnostics.h.
---------
Co-authored-by: Jacques Pienaar <jpienaar@google.com>
Relands #118583, with a fix for Python 3.8 compatibility. It was not
possible to set the buffer protocol accessers via slots in Python 3.8.
Why? https://nanobind.readthedocs.io/en/latest/why.html says it better
than I can, but my primary motivation for this change is to improve MLIR
IR construction time from JAX.
For a complicated Google-internal LLM model in JAX, this change improves
the MLIR
lowering time by around 5s (out of around 30s), which is a significant
speedup for simply switching binding frameworks.
To a large extent, this is a mechanical change, for instance changing
`pybind11::` to `nanobind::`.
Notes:
* this PR needs Nanobind 2.4.0, because it needs a bug fix
(https://github.com/wjakob/nanobind/pull/806) that landed in that
release.
* this PR does not port the in-tree dialect extension modules. They can
be ported in a future PR.
* I removed the py::sibling() annotations from def_static and def_class
in `PybindAdapters.h`. These ask pybind11 to try to form an overload
with an existing method, but it's not possible to form mixed
pybind11/nanobind overloads this ways and the parent class is now
defined in nanobind. Better solutions may be possible here.
* nanobind does not contain an exact equivalent of pybind11's buffer
protocol support. It was not hard to add a nanobind implementation of a
similar API.
* nanobind is pickier about casting to std::vector<bool>, expecting that
the input is a sequence of bool types, not truthy values. In a couple of
places I added code to support truthy values during casting.
* nanobind distinguishes bytes (`nb::bytes`) from strings (e.g.,
`std::string`). This required nb::bytes overloads in a few places.
Why? https://nanobind.readthedocs.io/en/latest/why.html says it better
than I can, but my primary motivation for this change is to improve MLIR
IR construction time from JAX.
For a complicated Google-internal LLM model in JAX, this change improves
the MLIR
lowering time by around 5s (out of around 30s), which is a significant
speedup for simply switching binding frameworks.
To a large extent, this is a mechanical change, for instance changing
`pybind11::`
to `nanobind::`.
Notes:
* this PR needs Nanobind 2.4.0, because it needs a bug fix
(https://github.com/wjakob/nanobind/pull/806) that landed in that
release.
* this PR does not port the in-tree dialect extension modules. They can
be ported in a future PR.
* I removed the py::sibling() annotations from def_static and def_class
in `PybindAdapters.h`. These ask pybind11 to try to form an overload
with an existing method, but it's not possible to form mixed
pybind11/nanobind overloads this ways and the parent class is now
defined in nanobind. Better solutions may be possible here.
* nanobind does not contain an exact equivalent of pybind11's buffer
protocol support. It was not hard to add a nanobind implementation of a
similar API.
* nanobind is pickier about casting to std::vector<bool>, expecting that
the input is a sequence of bool types, not truthy values. In a couple of
places I added code to support truthy values during casting.
* nanobind distinguishes bytes (`nb::bytes`) from strings (e.g.,
`std::string`). This required nb::bytes overloads in a few places.
The MLIR C and Python Bindings expose various methods from
`mlir::OpPrintingFlags` . This PR adds a binding for the `skipRegions`
method, which allows to skip the printing of Regions when printing Ops.
It also exposes this option as parameter in the python `get_asm` and
`print` methods
Following a rather direct approach to expose PDL usage from C and then
Python. This doesn't yes plumb through adding support for custom
matchers through this interface, so constrained to basics initially.
This also exposes greedy rewrite driver. Only way currently to define
patterns is via PDL (just to keep small). The creation of the PDL
pattern module could be improved to avoid folks potentially accessing
the module used to construct it post construction. No ergonomic work
done yet.
---------
Signed-off-by: Jacques Pienaar <jpienaar@google.com>
When an operation is erased in Python, its children may still be in the
"live" list inside Python bindings. After this, if some of the newly
allocated operations happen to reuse the same pointer address, this will
trigger an assertion in the bindings. This assertion would be incorrect
because the operations aren't actually live. Make sure we remove the
children operations from the "live" list when erasing the parent.
This also concentrates responsibility over the removal from the "live"
list and invalidation in a single place.
Note that this requires the IR to be sufficiently structurally valid so
a walk through it can succeed. If this invariant was broken by, e.g, C++
pass called from Python, there isn't much we can do.
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.
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}")
```
This reverts commit bbc29768683b394b34600347f46be2b8245ddb30.
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.
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>
This PR adds "value casting", i.e., a mechanism to wrap `ir.Value` in a
proxy class that overloads dunders such as `__add__`, `__sub__`, and
`__mul__` for fun and great profit.
This is thematically similar to
bfb1ba7526
and
9566ee2806.
The example in the test demonstrates the value of the feature (no pun
intended):
```python
@register_value_caster(F16Type.static_typeid)
@register_value_caster(F32Type.static_typeid)
@register_value_caster(F64Type.static_typeid)
@register_value_caster(IntegerType.static_typeid)
class ArithValue(Value):
__add__ = partialmethod(_binary_op, op="add")
__sub__ = partialmethod(_binary_op, op="sub")
__mul__ = partialmethod(_binary_op, op="mul")
a = arith.constant(value=FloatAttr.get(f16_t, 42.42))
b = a + a
# CHECK: ArithValue(%0 = arith.addf %cst, %cst : f16)
print(b)
a = arith.constant(value=FloatAttr.get(f32_t, 42.42))
b = a - a
# CHECK: ArithValue(%1 = arith.subf %cst_0, %cst_0 : f32)
print(b)
a = arith.constant(value=FloatAttr.get(f64_t, 42.42))
b = a * a
# CHECK: ArithValue(%2 = arith.mulf %cst_1, %cst_1 : f64)
print(b)
```
**EDIT**: this now goes through the bindings and thus supports automatic
casting of `OpResult` (including as an element of `OpResultList`),
`BlockArgument` (including as an element of `BlockArgumentList`), as
well as `Value`.
`PyOperations` are Python-level handles to `Operation *` instances. When
the latter are modified by C++, the former need to be invalidated.
#69746 implements such invalidation mechanism by setting all
`PyReferences` to `invalid`. However, that is not enough: they also need
to be removed from the `liveOperations` map since other parts of the
code (such as `PyOperation::createDetached`) assume that that map only
contains valid refs.
This is required to actually solve the issue in #69730.
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.
The reason I want this is that I am writing my own Python bindings and
would like to use the insertion point from
`PyThreadContextEntry::getDefaultInsertionPoint()` to call C++ functions
that take an `OpBuilder` (I don't need to expose it in Python but it
also seems appropriate). AFAICT, there is currently no way to translate
a `PyInsertionPoint` into an `OpBuilder` because the operation is
inaccessible.
This does basic plumbing, ideally want a context approach to reduce
needing to thread these manually, but the current is useful even in that
state.
Made Value.get_name change backwards compatible, so one could either set
a field or create a state to pass in.
This reduces code generated for type inference and instead reuses
facilities CAPI side that performed same role.
Differential Revision: https://reviews.llvm.org/D156041t
Update remaining `PyAttribute`-returning APIs to return `MlirAttribute` instead,
so that they go through the downcasting mechanism.
Reviewed By: makslevental
Differential Revision: https://reviews.llvm.org/D154462
depends on D150839
This diff uses `MlirTypeID` to register `TypeCaster`s (i.e., `[](PyType pyType) -> DerivedTy { return pyType; }`) for all concrete types (i.e., `PyConcrete<...>`) that are then queried for (by `MlirTypeID`) and called in `struct type_caster<MlirType>::cast`. The result is that anywhere an `MlirType mlirType` is returned from a python binding, that `mlirType` is automatically cast to the correct concrete type. For example:
```
c0 = arith.ConstantOp(f32, 0.0)
# CHECK: F32Type(f32)
print(repr(c0.result.type))
unranked_tensor_type = UnrankedTensorType.get(f32)
unranked_tensor = tensor.FromElementsOp(unranked_tensor_type, [c0]).result
# CHECK: UnrankedTensorType
print(type(unranked_tensor.type).__name__)
# CHECK: UnrankedTensorType(tensor<*xf32>)
print(repr(unranked_tensor.type))
```
This functionality immediately extends to typed attributes (i.e., `attr.type`).
The diff also implements similar functionality for `mlir_type_subclass`es but in a slightly different way - for such types (which have no cpp corresponding `class` or `struct`) the user must provide a type caster in python (similar to how `AttrBuilder` works) or in cpp as a `py::cpp_function`.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D150927
This diff adds python bindings for `MlirTypeID`. It paves the way for returning accurately typed `Type`s from python APIs (see D150927) and then further along building type "conscious" `Value` APIs (see D150413).
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D150839
Can't return a well-formed IR output while enabling version to be bumped
up during emission. Previously it would return min version but
potentially invalid IR which was confusing, instead make it return
error and abort immediately instead.
Differential Revision: https://reviews.llvm.org/D149569
Add method to set a desired bytecode file format to generate. Change
write method to be able to return status including the minimum bytecode
version needed by reader. This enables generating an older version of
the bytecode (not dialect ops, attributes or types). But this does not
guarantee that an older version can always be generated, e.g., if a
dialect uses a new encoding only available at later bytecode version.
This clamps setting to at most current version.
Differential Revision: https://reviews.llvm.org/D146555
This resolves some warnings when building with C++20, e.g.:
```
llvm-project/mlir/lib/Bindings/Python/IRAffine.cpp:545:60: warning: ISO C++20 considers use of overloaded operator '==' (with operand types 'mlir::python::PyAffineExpr' and 'mlir::python::PyAffineExpr') to be ambiguous despite there being a unique best viable function [-Wambiguous-reversed-operator]
PyAffineExpr &other) { return self == other; })
~~~~ ^ ~~~~~
llvm-project/mlir/lib/Bindings/Python/IRAffine.cpp:350:20: note: ambiguity is between a regular call to this operator and a call with the argument order reversed
bool PyAffineExpr::operator==(const PyAffineExpr &other) {
^
```
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D147018
This updates most (all?) error-diagnostic-emitting python APIs to
capture error diagnostics and include them in the raised exception's
message:
```
>>> Operation.parse('"arith.addi"() : () -> ()'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
mlir._mlir_libs.MLIRError: Unable to parse operation assembly:
error: "-":1:1: 'arith.addi' op requires one result
note: "-":1:1: see current operation: "arith.addi"() : () -> ()
```
The diagnostic information is available on the exception for users who
may want to customize the error message:
```
>>> try:
... Operation.parse('"arith.addi"() : () -> ()')
... except MLIRError as e:
... print(e.message)
... print(e.error_diagnostics)
... print(e.error_diagnostics[0].message)
...
Unable to parse operation assembly
[<mlir._mlir_libs._mlir.ir.DiagnosticInfo object at 0x7fed32bd6b70>]
'arith.addi' op requires one result
```
Error diagnostics captured in exceptions aren't propagated to diagnostic
handlers, to avoid double-reporting of errors. The context-level
`emit_error_diagnostics` option can be used to revert to the old
behaviour, causing error diagnostics to be reported to handlers instead
of as part of exceptions.
API changes:
- `Operation.verify` now raises an exception on verification failure,
instead of returning `false`
- The exception raised by the following methods has been changed to
`MLIRError`:
- `PassManager.run`
- `{Module,Operation,Type,Attribute}.parse`
- `{RankedTensorType,UnrankedTensorType}.get`
- `{MemRefType,UnrankedMemRefType}.get`
- `VectorType.get`
- `FloatAttr.get`
closes#60595
depends on D144804, D143830
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D143869
The raw `OpView` classes are used to bypass the constructors of `OpView`
subclasses, but having a separate class can create some confusing
behaviour, e.g.:
```
op = MyOp(...)
# fails, lhs is 'MyOp', rhs is '_MyOp'
assert type(op) == type(op.operation.opview)
```
Instead we can use `__new__` to achieve the same thing without a
separate class:
```
my_op = MyOp.__new__(MyOp)
OpView.__init__(my_op, op)
```
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D143830
Currently the bindings only allow for parsing IR with a top-level
`builtin.module` op, since the parse APIs insert an implicit module op.
This change adds `Operation.parse`, which returns whatever top-level op
is actually in the source.
To simplify parsing of specific operations, `OpView.parse` is also
added, which handles the error checking for `OpView` subclasses.
Reviewed By: ftynse, stellaraccident
Differential Revision: https://reviews.llvm.org/D143352
