This patch adds the `#gpu.kernel_metadata` and `#gpu.kernel_table`
attributes. The `#gpu.kernel_metadata` attribute allows storing metadata
related to a compiled kernel, for example, the number of scalar
registers used by the kernel. The attribute only has 2 required
parameters, the name and function type. It also has 2 optional
parameters, the arguments attributes and generic dictionary for storing
all other metadata.
The `#gpu.kernel_table` stores a table of `#gpu.kernel_metadata`,
mapping the name of the kernel to the metadata.
Finally, the function `ROCDL::getAMDHSAKernelsELFMetadata` was added to
collect ELF metadata from a binary, and to test the class methods in
both attributes.
Example:
```mlir
gpu.binary @binary [#gpu.object<#rocdl.target<chip = "gfx900">, kernels = #gpu.kernel_table<[
#gpu.kernel_metadata<"kernel0", (i32) -> (), metadata = {sgpr_count = 255}>,
#gpu.kernel_metadata<"kernel1", (i32, f32) -> (), arg_attrs = [{llvm.read_only}, {}]>
]> , bin = "BLOB">]
```
The motivation behind these attributes is to provide useful information
for things like tunning.
---------
Co-authored-by: Mehdi Amini <joker.eph@gmail.com>
This PR adds `f8E3M4` type to mlir.
`f8E3M4` type follows IEEE 754 convention
```c
f8E3M4 (IEEE 754)
- Exponent bias: 3
- Maximum stored exponent value: 6 (binary 110)
- Maximum unbiased exponent value: 6 - 3 = 3
- Minimum stored exponent value: 1 (binary 001)
- Minimum unbiased exponent value: 1 − 3 = −2
- Precision specifies the total number of bits used for the significand (mantissa),
including implicit leading integer bit = 4 + 1 = 5
- Follows IEEE 754 conventions for representation of special values
- Has Positive and Negative zero
- Has Positive and Negative infinity
- Has NaNs
Additional details:
- Max exp (unbiased): 3
- Min exp (unbiased): -2
- Infinities (+/-): S.111.0000
- Zeros (+/-): S.000.0000
- NaNs: S.111.{0,1}⁴ except S.111.0000
- Max normal number: S.110.1111 = +/-2^(6-3) x (1 + 15/16) = +/-2^3 x 31 x 2^(-4) = +/-15.5
- Min normal number: S.001.0000 = +/-2^(1-3) x (1 + 0) = +/-2^(-2)
- Max subnormal number: S.000.1111 = +/-2^(-2) x 15/16 = +/-2^(-2) x 15 x 2^(-4) = +/-15 x 2^(-6)
- Min subnormal number: S.000.0001 = +/-2^(-2) x 1/16 = +/-2^(-2) x 2^(-4) = +/-2^(-6)
```
Related PRs:
- [PR-99698](https://github.com/llvm/llvm-project/pull/99698) [APFloat]
Add support for f8E3M4 IEEE 754 type
- [PR-97118](https://github.com/llvm/llvm-project/pull/97118) [MLIR] Add
f8E4M3 IEEE 754 type
This PR adds `f8E4M3` type to mlir.
`f8E4M3` type follows IEEE 754 convention
```c
f8E4M3 (IEEE 754)
- Exponent bias: 7
- Maximum stored exponent value: 14 (binary 1110)
- Maximum unbiased exponent value: 14 - 7 = 7
- Minimum stored exponent value: 1 (binary 0001)
- Minimum unbiased exponent value: 1 − 7 = −6
- Precision specifies the total number of bits used for the significand (mantisa),
including implicit leading integer bit = 3 + 1 = 4
- Follows IEEE 754 conventions for representation of special values
- Has Positive and Negative zero
- Has Positive and Negative infinity
- Has NaNs
Additional details:
- Max exp (unbiased): 7
- Min exp (unbiased): -6
- Infinities (+/-): S.1111.000
- Zeros (+/-): S.0000.000
- NaNs: S.1111.{001, 010, 011, 100, 101, 110, 111}
- Max normal number: S.1110.111 = +/-2^(7) x (1 + 0.875) = +/-240
- Min normal number: S.0001.000 = +/-2^(-6)
- Max subnormal number: S.0000.111 = +/-2^(-6) x 0.875 = +/-2^(-9) x 7
- Min subnormal number: S.0000.001 = +/-2^(-6) x 0.125 = +/-2^(-9)
```
Related PRs:
- [PR-97179](https://github.com/llvm/llvm-project/pull/97179) [APFloat]
Add support for f8E4M3 IEEE 754 type
Similar to other attributes in Binding, the `PyAffineMapAttribute`
should include a value attribute to enable users to directly retrieve
the `AffineMap` from the `AffineMapAttr`.
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>
The PR implements MLIR Python Bindings for a few simple edit operations
on Block arguments, namely, `add_argument`, `erase_argument`, and
`erase_arguments`.
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 change adds bindings for `mlirDenseElementsAttrGet` which accepts a
list of MLIR attributes and constructs a DenseElementsAttr. This allows
for creating `DenseElementsAttr`s of types not natively supported by
Python (e.g. BF16) without requiring other dependencies (e.g. `numpy` +
`ml-dtypes`).
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.
If the python callback throws an error, the c++ code will throw a
py::error_already_set that needs to be caught and handled in the c++
code .
This change is inspired by the similar solution in
PySymbolTable::walkSymbolTables.
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.
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}")
```
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.
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.
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.
This reverts commit 0d109035c2.
Changes make Python bindings unbuildable without additional cmake
modifications (or modified `$PATH`).
```
/llvm-project/mlir/lib/Bindings/Python/IRCore.cpp:33:10: fatal error: 'funcobject.h' file not found
```
This header is provided by cpython, and we are not looking for that in
cmake.
Moreover, the nature of this change is not very clear to me. Seems to
replace one include with two dozens, presumably because the code is only
using transitively included headers, but the value for readability is
dubious. LLVM is also not strictly following IWYU.
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>
The existing initialization sequence always enables multi-threading at
MLIRContext construction time, making it impractical to provide a
customized thread pool.
Here, this is changed to always create the context with threading
disabled, process all site-specific init hooks (which can set thread
pools) and ultimately enable multi-threading unless if site-configured
to not do so.
This should preserve the existing user-visible initialization behavior
while also letting downstreams ensure that contexts are always created
with a shared thread pool. This was tested with IREE, which has such a
concept. Using site-specific thread tuning produced up to 2x single
compilation job behavior and customization of batch compilation (i.e. as
part of a build system) to utilize half the memory and run the entire
test suite ~2x faster. Given this, I believe that the additional
configurability can well pay for itself for implementations that use it.
We may also want to present user-level Python APIs for controlling
threading configuration in the future.
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`.
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.
