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.
The revision moves pack/unpack related patterns to
PackAndUnpackPatterns.cpp. This follows the convention like other tensor
ops.
It also renames `populateSimplifyTensorPack` to
`populateSimplifyPackAndUnpackPatterns` and adds a TODO item for
tensor.unpack op.
This commit reapplies #66987, which got original contained a memory leak
and got reverted by 78c8ab5844e618162c4cf3982d05102d4da10d23. The leak
is now fixed.
Original description:
This PR extends the error message of the tracking listener when
replacement ops cannot be found. That may happen if the applied patterns
replace an op by an op of a different kind or by block arguments.
However, this only matters if there are alive handles to the replaced
op. The new error message mentions that explicitly and reports the alive
handles.
This PR extends the error message of the tracking listener when
replacement ops cannot be found. That may happen if the applied patterns
replace an op by an op of a different kind or by block arguments.
However, this only matters if there are alive handles to the replaced
op. The new error message mentions that explicitly and reports the alive
handles.
In `TestTensorTransforms.cpp` `replaced` is nullptr I assumed the intent
was to emit the error for the `rootOp`.
In `TransformInterfaces.cpp` there were some uninitialized variables.
In `NVGPUTransformOps.cpp` `matmulOp` was never used.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D154439
This is useful for transformations such as bufferization, which is looking for tensor.extract_slice/insert_slice pairs.
Also fix the documentation of the corresponding tranform op.
Differential Revision: https://reviews.llvm.org/D152455
When looking for replacement ops (`findReplacementOp`) distinguish between "no replacement could be found" and "this op should be dropped from the mapping". The latter case will be utilized in a subsequent revision when a payload op is mapped to a consumed handle.
Differential Revision: https://reviews.llvm.org/D152375
Add a new interface `FindPayloadReplacementOpInterface` to specify ops that should be skipped when looking for payload replacement ops. Such ops are typically metadata-only ops.
With this change, we no longer need to maintain a custom TrackingListener in the tensor dialect.
Note: `CastOpInterface` by itself is not sufficient. Some metadata-only ops such as "tensor.reshape" are not casts, and it would be incorrect for them to implement the `CastOpInterface`.
Differential Revision: https://reviews.llvm.org/D151888
Certain InsertSliceOps, that do not use elements from the destination, are treated like casts when looking for replacement ops. Such InsertSliceOps are typically rank expansions.
Tensors with dynamic shape are not supported at the moment.
Also adds test cases for the TrackingListener.
Differential Revision: https://reviews.llvm.org/D151422
The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.
Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.
Caveats include:
- This clang-tidy script probably has more problems.
- This only touches C++ code, so nothing that is being generated.
Context:
- https://mlir.llvm.org/deprecation/ at "Use the free function variants
for dyn_cast/cast/isa/…"
- Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443
Implementation:
This first patch was created with the following steps. The intention is
to only do automated changes at first, so I waste less time if it's
reverted, and so the first mass change is more clear as an example to
other teams that will need to follow similar steps.
Steps are described per line, as comments are removed by git:
0. Retrieve the change from the following to build clang-tidy with an
additional check:
https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check
1. Build clang-tidy
2. Run clang-tidy over your entire codebase while disabling all checks
and enabling the one relevant one. Run on all header files also.
3. Delete .inc files that were also modified, so the next build rebuilds
them to a pure state.
4. Some changes have been deleted for the following reasons:
- Some files had a variable also named cast
- Some files had not included a header file that defines the cast
functions
- Some files are definitions of the classes that have the casting
methods, so the code still refers to the method instead of the
function without adding a prefix or removing the method declaration
at the same time.
```
ninja -C $BUILD_DIR clang-tidy
run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
-header-filter=mlir/ mlir/* -fix
rm -rf $BUILD_DIR/tools/mlir/**/*.inc
git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\
mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\
mlir/lib/**/IR/\
mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\
mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\
mlir/test/lib/Dialect/Test/TestTypes.cpp\
mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\
mlir/test/lib/Dialect/Test/TestAttributes.cpp\
mlir/unittests/TableGen/EnumsGenTest.cpp\
mlir/test/python/lib/PythonTestCAPI.cpp\
mlir/include/mlir/IR/
```
Differential Revision: https://reviews.llvm.org/D150123
These old patterns are not in use in either MLIR or downstream projects except for one test.
Additionally this is redundant with logic in the tensor.pad tiling implementation.
Drop SplitPaddingPatterns to reduce entropy.
Differential Revision: https://reviews.llvm.org/D148207
This change adds a new helper function `mlir::reifyResultShapes` that calls the corresponding interface method and also checks the result produced by the implementation when running in debug mode. Bugs due to incorrect interface implementations can be difficult to debug.
This helper function also reduces the amount of code needed at call sites: the cast to `ReifyRankedShapedTypeOpInterface` is done in the helper function.
Differential Revision: https://reviews.llvm.org/D145777
`reifyResultShapes` now returns `OpFoldResult`s instead of `Value`s. This is often more efficient because many transformations immediately attempt to extract a constant from the reified values.
Differential Revision: https://reviews.llvm.org/D145250
- Fold an unpack(pack(x)) to x.
- Rewrite a `tensor.pack` to an `tensor.expand_shape` if only one
dimension is packed.
Reviewed By: tyb0807, hanchung, mravishankar
Differential Revision: https://reviews.llvm.org/D141123
The tensor.pack ops have pad semantic, so we can fold pad + pack into
pack when
1. They have the same padding values or the pack op does not have
padding values.
2. The pad op does not have low paddings.
The tensor.unpack ops have extract_slice semantic, so we can fold unpack
+ extract_slice into unpack when
1. All the offsets are 0s.
2. All the strides are 1s.
Reviewed By: tyb0807
Differential Revision: https://reviews.llvm.org/D141099
At the moment, they are a part of EmptyOp::getCanonicalizationPatterns. When
extract_slice(tensor.empty) is rewritten as a new tensor.empty, it could
happen that we end up with two tensor.empty ops, since the original
tensor.empty can have two users. After bufferization such cases result in two
allocations.
Differential Revision: https://reviews.llvm.org/D139308
This fixes the case where scf::LoopNest::loops is empty.
Change LoopVector and ValueVector to SmallVector.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D136926
`scf.foreach_thread` defines mapping its loops to processors via an integer array, see an example below. A lowering can use this mapping. However, expressing mapping as an integer array is very confusing, especially when there are multiple levels of parallelism. In addition, the op does not verify the integer array. This change introduces device mapping attribute to make mapping descriptive and verifiable. Then it makes GPU transform dialect use it.
```
scf.foreach_thread (%i, %j) in (%c1, %c2) {
scf.foreach_thread (%i2, %j2) in (%c1, %c2)
{...} { thread_dim_mapping = [0, 1]}
} { thread_dim_mapping = [0, 1]}
```
It first introduces a `DeviceMappingInterface` which is an attribute interface. `scf.foreach_thread` defines its mapping via this interface. A lowering must define its attributes and implement this interface as well. This way gives us a clear validation.
The change also introduces two new attributes (`#gpu.thread<x/y/z>` and `#gpu.block<x,y,z>` ). After this change, the above code prints as below, as seen here, this way clarifies the loop mappings. The change also implements consuming of these two new attribute by the transform dialect. Transform dialect binds the outermost loops to the thread blocks and innermost loops to threads.
```
scf.foreach_thread (%i, %j) in (%c1, %c2) {
scf.foreach_thread (%i2, %j2) in (%c1, %c2)
{...} { thread_dim_mapping = [#gpu.thread<x>, #gpu.thread<y>]}
} { thread_dim_mapping = [#gpu.block<x>, #gpu.block<y>]}
```
Reviewed By: ftynse, nicolasvasilache
Differential Revision: https://reviews.llvm.org/D137413
Prior to this change, the "ExtractSliceFromReshape" pattern would transform
```
%collapsed = tensor.collapse_shape %input [[0, 1], [2]]
: tensor<1x11x100xf32> into tensor<11x100xf32>
%slice = tensor.extract_slice %collapsed [%offt, 0] [%size, 100] [1, 1]
: tensor<11x100xf32> to tensor<?x100xf32>
```
into a loop that iterated over the range `%size - %offt`, that pieces
together multiple sub-slices of `%input` along the first dimension. This
is correct but obviously inefficient. The technical condition is that
collapsing at-most-one non-unit dimension of `%src` will not result in a
subsequent slice along the corresponding dimension of `%collapsed`
mapping across discontinuities in the index space of `%src`. Thus, the
definition of a "linearized dimension" (from the perspective of
`tensor.collapse_shape`) is updated to reflect this condition.
The transform will now generate
```
%slice = tensor.extract_slice %input [0, %offt, 0][1, %size, 100] [1, 1]
: tensor<1x11x100xf32> to tensor<1x?x100xf32>
%result = tensor.collapse_shape [[0, 1], [2]]
: tensor<1x?x100xf32> to tensor<?x100xf32>
```
which can be further canonicalized.
Additional tests are added to check this family of edge cases.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D135726
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
Consecutive tensor.insert_slice/tensor.extract_slice can be
created for the case like tiling convolution and then downsizing
2-D convolutions into 1-D ones. It hinders further transformations.
So adding these patterns to clean it up.
Given that bufferization is sensitive and have requirements over
the IR structure (see https://reviews.llvm.org/D132666),
these patterns are put in Transforms/ with separate entry points
for explicit collection.
Reviewed By: ThomasRaoux, mravishankar
Differential Revision: https://reviews.llvm.org/D133871
The transformation would fail if none of the sliced dimensions were
linearized by the producing `tensor.collapse_shape`. This is a trivial
edge case but it wasn't correctly tested. Fixes the issue and adds a test.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D134088
This change adds a set of utilities to replace the result of a
`tensor.collapse_shape -> tensor.extract_slice` chain with the
equivalent result formed by aggregating slices of the
`tensor.collapse_shape` source. In general, it is not possible to
commute `extract_slice` and `collapse_shape` if linearized dimensions
are sliced. The i-th dimension of the `tensor.collapse_shape`
result is a "linearized sliced dimension" if:
1) Reassociation indices of tensor.collapse_shape in the i'th position
is greater than size 1 (multiple dimensions of the input are collapsed)
2) The i-th dimension is sliced by `tensor.extract_slice`.
We can work around this by stitching together the result of
`tensor.extract_slice` by iterating over any linearized sliced dimensions.
This is equivalent to "tiling" the linearized-and-sliced dimensions of
the `tensor.collapse_shape` operation in order to manifest the result
tile (the result of the `tensor.extract_slice`). The user of the
utilities must provide the mechanism to create the tiling (e.g. a loop).
In the tests, it is demonstrated how to apply the utilities using either
`scf.for` or `scf.foreach_thread`.
The below example illustrates the pattern using `scf.for`:
```
%0 = linalg.generic ... -> tensor<3x7x11x10xf32>
%1 = tensor.collapse_shape %0 [[0, 1, 2], [3]] : ... to tensor<341x10xf32>
%2 = tensor.extract_slice %1 [13, 0] [10, 10] [2, 1] : .... tensor<10x10xf32>
```
We can construct %2 by generating the following IR:
```
%dest = linalg.init_tensor() : tensor<10x10xf32>
%2 = scf.for %iv = %c0 to %c10 step %c1 iter_args(%arg0) -> tensor<10x10xf32> {
// Step 1: Map this output idx (%iv) to a multi-index for the input (%3):
%linear_index = affine.apply affine_map<(d0)[]->(d0*2 + 11)>(%iv)
%3:3 = arith.delinearize_index %iv into (3, 7, 11)
// Step 2: Extract the slice from the input
%4 = tensor.extract_slice %0 [%3#0, %3#1, %3#2, 0] [1, 1, 1, 10] [1, 1, 1, 1] :
tensor<3x7x11x10xf32> to tensor<1x1x1x10xf32>
%5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] :
tensor<1x1x1x10xf32> into tensor<1x10xf32>
// Step 3: Insert the slice into the destination
%6 = tensor.insert_slice %5 into %arg0 [%iv, 0] [1, 10] [1, 1] :
tensor<1x10xf32> into tensor<10x10xf32>
scf.yield %6 : tensor<10x10xf32>
}
```
The pattern was discussed in the RFC here: https://discourse.llvm.org/t/rfc-tensor-extracting-slices-from-tensor-collapse-shape/64034
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D129699
This reverts commit 5711957875738c1318f89afd7bf4be388f85a087.
A circular dependency is introduced here from Dialect/Utils/ to the
ViewLikeInterface, but it already depends on Dialect/Utils.
Also this introduces a dependency from lib/Dialect/Tensor to Linalg,
which isn't obviously correct from a layering point of view.
This change adds a set of utilities to replace the result of a
`tensor.collapse_shape -> tensor.extract_slice` chain with the
equivalent result formed by aggregating slices of the
`tensor.collapse_shape` source. In general, it is not possible to
commute `extract_slice` and `collapse_shape` if linearized dimensions
are sliced. The i-th dimension of the `tensor.collapse_shape`
result is a "linearized sliced dimension" if:
1) Reassociation indices of tensor.collapse_shape in the i'th position
is greater than size 1 (multiple dimensions of the input are collapsed)
2) The i-th dimension is sliced by `tensor.extract_slice`.
We can work around this by stitching together the result of
`tensor.extract_slice` by iterating over any linearized sliced dimensions.
This is equivalent to "tiling" the linearized-and-sliced dimensions of
the `tensor.collapse_shape` operation in order to manifest the result
tile (the result of the `tensor.extract_slice`). The user of the
utilities must provide the mechanism to create the tiling (e.g. a loop).
In the tests, it is demonstrated how to apply the utilities using either
`scf.for` or `scf.foreach_thread`.
The below example illustrates the pattern using `scf.for`:
```
%0 = linalg.generic ... -> tensor<3x7x11x10xf32>
%1 = tensor.collapse_shape %0 [[0, 1, 2], [3]] : ... to tensor<341x10xf32>
%2 = tensor.extract_slice %1 [13, 0] [10, 10] [2, 1] : .... tensor<10x10xf32>
```
We can construct %2 by generating the following IR:
```
%dest = linalg.init_tensor() : tensor<10x10xf32>
%2 = scf.for %iv = %c0 to %c10 step %c1 iter_args(%arg0) -> tensor<10x10xf32> {
// Step 1: Map this output idx (%iv) to a multi-index for the input (%3):
%linear_index = affine.apply affine_map<(d0)[]->(d0*2 + 11)>(%iv)
%3:3 = arith.delinearize_index %iv into (3, 7, 11)
// Step 2: Extract the slice from the input
%4 = tensor.extract_slice %0 [%3#0, %3#1, %3#2, 0] [1, 1, 1, 10] [1, 1, 1, 1] :
tensor<3x7x11x10xf32> to tensor<1x1x1x10xf32>
%5 = tensor.collapse_shape %4 [[0, 1, 2], [3]] :
tensor<1x1x1x10xf32> into tensor<1x10xf32>
// Step 3: Insert the slice into the destination
%6 = tensor.insert_slice %5 into %arg0 [%iv, 0] [1, 10] [1, 1] :
tensor<1x10xf32> into tensor<10x10xf32>
scf.yield %6 : tensor<10x10xf32>
}
```
The pattern was discussed in the RFC here: https://discourse.llvm.org/t/rfc-tensor-extracting-slices-from-tensor-collapse-shape/64034
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D129699
This aligns the SCF dialect file layout with the majority of the dialects.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D128049
This commit restructures how TypeID is implemented to ideally avoid
the current problems related to shared libraries. This is done by changing
the "implicit" fallback path to use the name of the type, instead of using
a static template variable (which breaks shared libraries). The major downside to this
is that it adds some additional initialization costs for the implicit path. Given the
use of type names for uniqueness in the fallback, we also no longer allow types
defined in anonymous namespaces to have an implicit TypeID. To simplify defining
an ID for these classes, a new `MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID` macro
was added to allow for explicitly defining a TypeID directly on an internal class.
To help identify when types are using the fallback, `-debug-only=typeid` can be
used to log which types are using implicit ids.
This change generally only requires changes to the test passes, which are all defined
in anonymous namespaces, and thus can't use the fallback any longer.
Differential Revision: https://reviews.llvm.org/D122775
A lot of test passes are currently anchored on FuncOp, but this
dependency
is generally just historical. A majority of these test passes can run on
any operation, or can operate on a specific interface
(FunctionOpInterface/SymbolOpInterface).
This allows for greatly reducing the API dependency on FuncOp, which
is slated to be moved out of the Builtin dialect.
Differential Revision: https://reviews.llvm.org/D121191
Add a pattern matcher for ExtractSliceOp when its source is a constant.
The matching heuristics can be governed by the control function since
generating a new constant is not always beneficial.
Differential Revision: https://reviews.llvm.org/D119605
This commit adds a pattern to wrap a tensor.pad op with
an scf.if op to separate the cases where we don't need padding
(all pad sizes are actually zeros) and where we indeed need
padding.
This pattern is meant to handle padding inside tiled loops.
Under such cases the padding sizes typically depend on the
loop induction variables. Splitting them would allow treating
perfect tiles and edge tiles separately.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D117018
