Extend linalg.pack and linalg.unpack to accept memref operands in
addition to tensors. As part of this change, we now disable all
transformations when these ops have memref semantics.
Closes https://github.com/llvm/llvm-project/issues/129004
---------
Signed-off-by: Ryutaro Okada <1015ryu88@gmail.com>
Co-authored-by: Hyunsung Lee <ita9naiwa@gmail.com>
Fixes: #164800
Ensures unsigned pooling ops in Linalg stay in the integer domain: the
lowering now rejects floating/bool inputs with a clear diagnostic, new
regression tests lock in both the error path and a valid integer
example, and transform decompositions are updated to reflect the integer
typing.
Signed-off-by: Akimasa Watanuki <mencotton0410@gmail.com>
This PR exposes `linalg::inferContractionDims(ArrayRef<AffineMap>)` to
Python, allowing users to infer contraction dimensions (batch/m/n/k)
directly from a list of affine maps without needing an operation.
---------
Signed-off-by: Bangtian Liu <liubangtian@gmail.com>
RFC:
https://discourse.llvm.org/t/rfc-deprecate-linalg-elemwise-unary-and-elemwise-binary/87144
Remove the two operations and fix the tests by:
* Cleaning simple operation tests of the old ops
* Changing `linalg.elemwise_{u|bi}nary` with `linalg.{exp|add}` on
transform tests
* Changing some of the tests with `linalg.elementwise` instead, to
broaden test coverage
* Surgically removing the `elemwise_*` part in the Python tests
* Update MLIR transform examples (text and tests) with
`linalg.elementwise` instead
Nothing else changed.
…_reduce_matmul.
This patch exposes broadcast and transpose semantics on
'batch_reduce_matmul'. This is the last one in continuation of other two
variant of matmul ops.
The broadcast and transpose semantic are as follows:
Broadcast and Transpose semantics can be appiled by specifying the
explicit attribute 'indexing_maps' as shown below. This is a list
attribute, so must include maps for all arguments if specified.
Example Transpose:
```
linalg.batch_reduce_matmul indexing_maps = [
affine_map<(d0, d1, d2, d3) -> (d0, d3, d1)>, // transpose
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
affine_map<(d0, d1, d2, d3) -> (d1, d2)>
]
ins(%arg0, %arg1 : memref<2x5x3xf32>,memref<2x5x7xf32>)
outs(%arg2: memref<3x7xf32>)
```
Example Broadcast:
```
linalg.batch_reduce_matmul indexing_maps = [
affine_map<(d0, d1, d2, d3) -> (d3)>, // broadcast
affine_map<(d0, d1, d2, d3) -> (d0, d3, d2)>,
affine_map<(d0, d1, d2, d3) -> (d1, d2)>
]
ins(%arg0, %arg1 : memref<5xf32>, memref<2x5x7xf32>)
outs(%arg2: memref<3x7xf32>)
```
Example Broadcast and Transpose:
```
linalg.batch_reduce_matmul indexing_maps = [
affine_map<(d0, d1, d2, d3) -> (d1, d3)>, // broadcast
affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>, // transpose
affine_map<(d0, d1, d2, d3) -> (d1, d2)>
]
ins(%arg0, %arg1 : memref<3x5xf32>, memref<2x7x5xf32>)
outs(%arg2: memref<3x7xf32>)
```
RFCs and related PR:
https://discourse.llvm.org/t/rfc-linalg-opdsl-constant-list-attribute-definition/80149https://discourse.llvm.org/t/rfc-op-explosion-in-linalg/82863https://discourse.llvm.org/t/rfc-mlir-linalg-operation-tree/83586https://github.com/llvm/llvm-project/pull/115319https://github.com/llvm/llvm-project/pull/122275
This PR is mainly about exposing the python bindings for
`linalg::isaConvolutionOpInterface` and `linalg::inferConvolutionDims`.
---------
Signed-off-by: Bangtian Liu <liubangtian@gmail.com>
This PR is mainly about exposing the python bindings for`
linalg::isaContractionOpInterface` and` linalg::inferContractionDims`.
---------
Signed-off-by: Bangtian Liu <liubangtian@gmail.com>
This PR https://github.com/llvm/llvm-project/pull/123902 broke python
bindings for `tensor.pack`/`unpack`. This PR fixes that. It also
1. adds convenience wrappers for pack/unpack
2. cleans up matmul-like ops in the linalg bindings
3. fixes linalg docs missing pack/unpack
As linalg.batch_matmul has been moved into tablegen from OpDSL, its
derived python wrapper no longer exist.This patch adds the required
python wrapper.
Also refactors the BatchmatmulOp printer to make it consistent with its
parser.
Now that linalg.matmul is in tablegen, "hand write" the Python wrapper
that OpDSL used to derive. Similarly, add a Python wrapper for the new
linalg.contract op.
Required following misc. fixes:
1) make linalg.matmul's parsing and printing consistent w.r.t. whether
indexing_maps occurs before or after operands, i.e. per the tests cases
it comes _before_.
2) tablegen for linalg.contract did not state it accepted an optional
cast attr.
3) In ODS's C++-generating code, expand partial support for `$_builder`
access in `Attr::defaultValue` to full support. This enables access to
the current `MlirContext` when constructing the default value (as is
required when the default value consists of affine maps).
The earlier PR(https://github.com/llvm/llvm-project/pull/104783) which
introduces
transpose and broadcast semantic to linalg.matmul was reverted due to
two failing
OpDSL test for linalg.matmul.
Since linalg.matmul is now defined using TableGen ODS instead of
Python-based OpDSL,
these test started failing and needs to be removed/updated.
This commit removes/updates the failing obsolete tests from below files.
All other files
were part of earlier PR and just cherry picked.
"mlir/test/python/integration/dialects/linalg/opsrun.py"
"mlir/test/python/integration/dialects/transform.py"
---------
Co-authored-by: Renato Golin <rengolin@systemcall.eu>
This reverts commit 03483737a7a2d72a257a5ab6ff01748ad9cf0f75 and
99c8557, which is a fix-up on top of the former.
I'm reverting because this commit broke two tests:
mlir/test/python/integration/dialects/linalg/opsrun.py
mlir/test/python/integration/dialects/transform.py
See https://lab.llvm.org/buildbot/#/builders/138/builds/4872
I'm not familiar with the tests, so I'm leaving it to the original author
to either remove or adapt the broken tests, as discussed here:
https://github.com/llvm/llvm-project/pull/104783#issuecomment-2406390905
The main goal of this patch is to extend the semantic of 'linalg.matmul'
named op to include per operand transpose semantic while also laying out
a way to move ops definition from OpDSL to tablegen. Hence, it is
implemented in tablegen. Transpose semantic is as follows.
By default 'linalg.matmul' behavior will remain as is. Transpose
semantics can be appiled on per input operand by specifying the optional
permutation attributes (namely 'permutationA' for 1st input and
'permutationB' for 2nd input) for each operand explicitly as needed. By
default, no transpose is mandated for any of the input operand.
Example:
```
%val = linalg.matmul ins(%arg0, %arg1 : memref<5x3xf32>,
memref<5x7xf32>)
outs(%arg2: memref<3x7xf32>)
permutationA = [1, 0]
permutationB = [0, 1]
```
Since we have extended `EmptyOp`, maybe we should also provide a
corresponding `tensor.empty` method. In the downstream usage, I tend to
use APIs with all lowercase letters to create ops, so having a
`tensor.empty` to replace the extended `tensor.EmptyOp` would keep my
code style consistent.
The following logic can lead to a class name mismatch when using
`linalg.powf` in Python. This PR fixed the issue and also renamed
`NegfOp` to `NegFOp` in linalg to adhere to the naming convention, as
exemplified by `arith::NegFOp`.
173514d58e/mlir/python/mlir/dialects/linalg/opdsl/lang/dsl.py (L140-L143)
```
# linalg.powf(arg0, arg1, outs=[init_result.result])
NotImplementedError: Unknown named op_name / op_class_name: powf / PowfOp
```
Currently, `linalg.transpose` and `linalg.broadcast` can't be emitted
through either the C API or the python bindings (which of course go
through the C API). See
https://discourse.llvm.org/t/how-to-build-linalg-transposeop-in-mlir-pybind/73989/10.
The reason is even though they're named ops, there is no opdsl
`@linalg_structured_op` for them and thus while they can be instantiated
they cannot be passed to
[`mlirLinalgFillBuiltinNamedOpRegion`](a7cccb9cbb/mlir/lib/CAPI/Dialect/Linalg.cpp (L18)).
I believe the issue is they both take a `IndexAttrDef` but
`IndexAttrDef` cannot represent dynamic rank. Note, if I'm mistaken and
there is a way to write the `@linalg_structured_op` let me know.
The solution here simply implements the `regionBuilder` interface which
is then picked up by
[`LinalgDialect::addNamedOpBuilders`](7557530f42/mlir/lib/Dialect/Linalg/IR/LinalgDialect.cpp (L116)).
Extension classes are added "by hand" that mirror the API of the
`@linalg_structured_op`s. Note, the extension classes are added to to
`dialects/linalg/__init__.py` instead of
`dialects/linalg/opdsl/ops/core_named_ops.py` in order that they're not
confused for opdsl generators/emitters.
This patch is part of a larger initiative aimed at fixing floating-point `max` and `min` operations in MLIR: https://discourse.llvm.org/t/rfc-fix-floating-point-max-and-min-operations-in-mlir/72671.
This commit addresses Task 1.2 of the mentioned RFC. By renaming these operations, we align their names with LLVM intrinsics that have corresponding semantics.
This renaming started with the native ODS support for properties, this is completing it.
A mass automated textual rename seems safe for most codebases.
Drop also the ods prefix to keep the accessors the same as they were before
this change:
properties.odsOperandSegmentSizes
reverts back to:
properties.operandSegementSizes
The ODS prefix was creating divergence between all the places and make it harder to
be consistent.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D157173
The operand_segment_sizes and result_segment_sizes Attributes are now inlined
in the operation as native propertie. We continue to support building an
Attribute on the fly for `getAttr("operand_segment_sizes")` and setting the
property from an attribute with `setAttr("operand_segment_sizes", attr)`.
A new bytecode version is introduced to support backward compatibility and
backdeployments.
Differential Revision: https://reviews.llvm.org/D155919
The operand_segment_sizes and result_segment_sizes Attributes are now inlined
in the operation as native propertie. We continue to support building an
Attribute on the fly for `getAttr("operand_segment_sizes")` and setting the
property from an attribute with `setAttr("operand_segment_sizes", attr)`.
A new bytecode version is introduced to support backward compatibility and
backdeployments.
Differential Revision: https://reviews.llvm.org/D155919
This is an ongoing series of commits that are reformatting our
Python code.
Reformatting is done with `black`.
If you end up having problems merging this commit because you
have made changes to a python file, the best way to handle that
is to run git checkout --ours <yourfile> and then reformat it
with black.
If you run into any problems, post to discourse about it and
we will try to help.
RFC Thread below:
https://discourse.llvm.org/t/rfc-document-and-standardize-python-code-style
Differential Revision: https://reviews.llvm.org/D150782
tensor.empty/linalg.init_tensor produces an uninititalized tensor that can be used as a destination operand for destination-style ops (ops that implement `DestinationStyleOpInterface`).
This change makes it possible to implement `TilingInterface` for non-destination-style ops without depending on the Linalg dialect.
RFC: https://discourse.llvm.org/t/rfc-add-tensor-from-shape-operation/65101
Differential Revision: https://reviews.llvm.org/D135129
This reland includes changes to the Python bindings.
Switch variadic operand and result segment size attributes to use the
dense i32 array. Dense integer arrays were introduced primarily to
represent index lists. They are a better fit for segment sizes than
dense elements attrs.
Depends on D131801
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D131803
Switch variadic operand and result segment size attributes to use the
dense i32 array. Dense integer arrays were introduced primarily to
represent index lists. They are a better fit for segment sizes than
dense elements attrs.
Depends on D131738
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D131702
This commit moves FuncOp out of the builtin dialect, and into the Func
dialect. This move has been planned in some capacity from the moment
we made FuncOp an operation (years ago). This commit handles the
functional aspects of the move, but various aspects are left untouched
to ease migration: func::FuncOp is re-exported into mlir to reduce
the actual API churn, the assembly format still accepts the unqualified
`func`. These temporary measures will remain for a little while to
simplify migration before being removed.
Differential Revision: https://reviews.llvm.org/D121266
The revision removes the linalg.fill operation and renames the OpDSL generated linalg.fill_tensor operation to replace it. After the change, all named structured operations are defined via OpDSL and there are no handwritten operations left.
A side-effect of the change is that the pretty printed form changes from:
```
%1 = linalg.fill(%cst, %0) : f32, tensor<?x?xf32> -> tensor<?x?xf32>
```
changes to
```
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
```
Additionally, the builder signature now takes input and output value ranges as it is the case for all other OpDSL operations:
```
rewriter.create<linalg::FillOp>(loc, val, output)
```
changes to
```
rewriter.create<linalg::FillOp>(loc, ValueRange{val}, ValueRange{output})
```
All other changes remain minimal. In particular, the canonicalization patterns are the same and the `value()`, `output()`, and `result()` methods are now implemented by the FillOpInterface.
Depends On D120726
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120728
Add operations abs, ceil, floor, and neg to the C++ API and Python API.
Add test cases.
Reviewed By: gysit
Differential Revision: https://reviews.llvm.org/D121339
Allow pointwise operations to take rank zero input tensors similarly to scalar inputs. Use an empty indexing map to broadcast rank zero tensors to the iteration domain of the operation.
Depends On D120734
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120807
Simplify tests that use `linalg.fill_rng_2d` to focus on testing the `const` and `index` functions. Additionally, cleanup emit_misc.py to use simpler test functions and fix an error message in config.py.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120734
Extend OpDSL with a `defines` method that can set the `hasCanonicalizer` flag for an OpDSL operation. If the flag is set via `defines(Canonicalizer)` the operation needs to implement the `getCanonicalizationPatterns` method. The revision specifies the flag for linalg.fill_tensor and adds an empty `FillTensorOp::getCanonicalizationPatterns` implementation.
This revision is a preparation step to replace linalg.fill by its OpDSL counterpart linalg.fill_tensor. The two are only functionally equivalent if both specify the same canonicalization patterns. The revision is thus a prerequisite for the linalg.fill replacement.
Depends On D120725
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120726
The last remaining operations in the standard dialect all revolve around
FuncOp/function related constructs. This patch simply handles the initial
renaming (which by itself is already huge), but there are a large number
of cleanups unlocked/necessary afterwards:
* Removing a bunch of unnecessary dependencies on Func
* Cleaning up the From/ToStandard conversion passes
* Preparing for the move of FuncOp to the Func dialect
See the discussion at https://discourse.llvm.org/t/standard-dialect-the-final-chapter/6061
Differential Revision: https://reviews.llvm.org/D120624
The revision renames the following OpDSL functions:
```
TypeFn.cast -> TypeFn.cast_signed
BinaryFn.min -> BinaryFn.min_signed
BinaryFn.max -> BinaryFn.max_signed
```
The corresponding enum values on the C++ side are renamed accordingly:
```
#linalg.type_fn<cast> -> #linalg.type_fn<cast_signed>
#linalg.binary_fn<min> -> #linalg.binary_fn<min_signed>
#linalg.binary_fn<max> -> #linalg.binary_fn<max_signed>
```
Depends On D120110
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120562
The revision extends OpDSL with unary and binary function attributes. A function attribute, makes the operations used in the body of a structured operation configurable. For example, a pooling operation may take an aggregation function attribute that specifies if the op shall implement a min or a max pooling. The goal of this revision is to define less and more flexible operations.
We may thus for example define an element wise op:
```
linalg.elem(lhs, rhs, outs=[out], op=BinaryFn.mul)
```
If the op argument is not set the default operation is used.
Depends On D120109
Reviewed By: nicolasvasilache, aartbik
Differential Revision: https://reviews.llvm.org/D120110
Split arithmetic function into unary and binary functions. The revision prepares the introduction of unary and binary function attributes that work similar to type function attributes.
Depends On D120108
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120109
Prepare the OpDSL function handling to introduce more function classes. A follow up commit will split ArithFn into UnaryFn and BinaryFn. This revision prepares the split by adding a function kind enum to handle different function types using a single class on the various levels of the stack (for example, there is now one TensorFn and one ScalarFn).
Depends On D119718
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120108
Previously, OpDSL operation used hardcoded type conversion operations (cast or cast_unsigned). Supporting signed and unsigned casts thus meant implementing two different operations. Type function attributes allow us to define a single operation that has a cast type function attribute which at operation instantiation time may be set to cast or cast_unsigned. We may for example, defina a matmul operation with a cast argument:
```
@linalg_structured_op
def matmul(A=TensorDef(T1, S.M, S.K), B=TensorDef(T2, S.K, S.N), C=TensorDef(U, S.M, S.N, output=True),
cast=TypeFnAttrDef(default=TypeFn.cast)):
C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n])
```
When instantiating the operation the attribute may be set to the desired cast function:
```
linalg.matmul(lhs, rhs, outs=[out], cast=TypeFn.cast_unsigned)
```
The revsion introduces a enum in the Linalg dialect that maps one-by-one to the type functions defined by OpDSL.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D119718
Index attributes had no default value, which means the attribute values had to be set on the operation. This revision adds a default parameter to `IndexAttrDef`. After the change, every index attribute has to define a default value. For example, we may define the following strides attribute:
```
```
When using the operation the default stride is used if the strides attribute is not set. The mechanism is implemented using `DefaultValuedAttr`.
Additionally, the revision uses the naming index attribute instead of attribute more consistently, which is a preparation for follow up revisions that will introduce function attributes.
Depends On D119125
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D119126
Previously, OpDSL did not support rank polymorphism, which required a separate implementation of linalg.fill. This revision extends OpDSL to support rank polymorphism for a limited class of operations that access only scalars and tensors of rank zero. At operation instantiation time, it scales these scalar computations to multi-dimensional pointwise computations by replacing the empty indexing maps with identity index maps. The revision does not change the DSL itself, instead it adapts the Python emitter and the YAML generator to generate different indexing maps and and iterators depending on the rank of the first output.
Additionally, the revision introduces a `linalg.fill_tensor` operation that in a future revision shall replace the current handwritten `linalg.fill` operation. `linalg.fill_tensor` is thus only temporarily available and will be renamed to `linalg.fill`.
Reviewed By: nicolasvasilache, stellaraccident
Differential Revision: https://reviews.llvm.org/D119003
The revision distinguishes `ReduceFn` and `ReduceFnUse`. The latter has the reduction dimensions attached while the former specifies the arithmetic function only. This separation allows us to adapt the reduction syntax a little bit and specify the reduction dimensions using square brackets (in contrast to the round brackets used for the values to reduce). It als is a preparation to add reduction function attributes to OpDSL. A reduction function attribute shall only specify the arithmetic function and not the reduction dimensions.
Example:
```
ReduceFn.max_unsigned(D.kh, D.kw)(...)
```
changes to:
```
ReduceFn.max_unsigned[D.kh, D.kw](...)
```
Depends On D115240
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D115241
