Previously this was only populated in the create method later. This
resolves some of invalid builder paths. This may also be sufficient that
type inference functions no longer have to consider whether property
conversion has happened (but haven't verified that yet).
This also makes Attributes corresponding to Properties as optional
inside the set from attributes method. Today that is in effect what
happens with Property value initialization and folks use it to define
custom C++ types whose default initialization is what they want. This is
the behavior users get if they use properties directly. Propagating
Attributes without allowing partial setting would require iterating over
the dictionary attribute considering the properties of the op type that
will be created. This could also have been an additional method
generated or optional behavior on the set method. But doing it
consistently seems better. In terms of whats lost, it doesn't seem like
anything compared to the pure Property path where Property is default
value initialized and then partially overwritten (this doesn't seem to
buy anything else verification wise).
Default valued Properties (as specified ODS side rather than C++ side)
triggered error as the containing class was not yet complete but
referenced nested class, so that we couldn't have default initializer
for them in the parent class. Added an additional forwarding builder to
avoid needing to update call sites. This could be split out to separate
change.
Inlined templated function in unit test that was only used once. Moved
initialization earlier where seen.
Windows build of `mlir` with Visual Studio (19.36.32538 for x64) using
with the following command:
`cmake.exe -GNinja -DCMAKE_BUILD_TYPE=Release
-DLLVM_ENABLE_PROJECTS=mlir -DLLVM_ENABLE_EH=ON -DLLVM_ENABLE_RTTI=1
-DLLVM_TARGETS_TO_BUILD=host ../llvm`
is leading to a crash when calling canonicalization on
`tensor.pack`/`tensor.unpack` ops `mlir-opt --canonicalize input.mlir`
where the `input.mlir` is as follows (this is taken from one of the
filecheck tests for `tensor.pack`):
```
func.func @pack_unpack(%arg0: tensor<128x256xf32>) -> tensor<128x256xf32> {
%pack_dest = tensor.empty() : tensor<8x16x8x32xf32>
%unpack_dest = tensor.empty() : tensor<128x256xf32>
%tp = tensor.pack %arg0 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %pack_dest : tensor<128x256xf32> -> tensor<8x16x8x32xf32>
%tup = tensor.unpack %tp outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %unpack_dest : tensor<8x16x8x32xf32> -> tensor<128x256xf32>
return %tup : tensor<128x256xf32>
}
```
The crash is seemingly coming from invalid memory access during
iterating over `innerDimsPos` within `getPackOpResultTypeShape`.
This crash is also causing the following tests to fail:
```
MLIR :: Dialect/Linalg/canonicalize.mlir
MLIR :: Dialect/Linalg/data-layout-propagation.mlir
MLIR :: Dialect/Linalg/generalize-tensor-pack-tile.mlir
MLIR :: Dialect/Linalg/generalize-tensor-pack.mlir
MLIR :: Dialect/Linalg/generalize-tensor-unpack-tile.mlir
MLIR :: Dialect/Linalg/generalize-tensor-unpack.mlir
MLIR :: Dialect/Linalg/transform-lower-pack.mlir
MLIR :: Dialect/Linalg/transform-op-fuse.mlir
MLIR :: Dialect/Linalg/transform-op-pack.mlir
MLIR :: Dialect/Linalg/transform-pack-greedily.mlir
MLIR :: Dialect/Tensor/canonicalize.mlir
MLIR :: Dialect/Tensor/fold-into-pack-and-unpack.mlir
MLIR :: Dialect/Tensor/invalid.mlir
MLIR :: Dialect/Tensor/ops.mlir
MLIR :: Dialect/Tensor/simplify-pack-unpack.mlir
MLIR :: Dialect/Tensor/tiling.mlir
```
A general question is: is it possible to support hooks here to infer the
encoding? E.g., when the extracted tensor slice is rank-reduced, the
encoding need to be updated accordingly as well.
In some cases this pattern may ignore static information due to dynamic
operands in the insert_slice sizes operands, e.g.:
```
%0 = tensor.cast %arg0 : tensor<1x?xf32> to tensor<?x?xf32>
%1 = tensor.insert_slice %0 into %arg1[...] [%s0, %s1] [...]
: tensor<?x?xf32> into tensor<?x?xf32>
```
Can be rewritten into:
```
%1 = tensor.insert_slice %arg0 into %arg1[...] [1, %s1] [...]
: tensor<1x?xf32> into tensor<?x?xf32>
```
This PR updates the matching in the pattern to allow rewrites like this.
Unblocking downstream integrate where an expected-to-fail test was
expecting this to be a runtime verifier error, not a compiler crash:
https://github.com/llvm/torch-mlir/pull/3279.
This patch generalizes tensor.expand_shape and memref.expand_shape to
consume the output shape as a list of SSA values. This enables us to
implement generic reshape operations with dynamic shapes using
collapse_shape/expand_shape pairs.
The output_shape input to expand_shape follows the static/dynamic
representation that's also used in `tensor.extract_slice`.
Differential Revision: https://reviews.llvm.org/D140821
---------
Signed-off-by: Gaurav Shukla<gaurav.shukla@amd.com>
Signed-off-by: Gaurav Shukla <gaurav.shukla@amd.com>
Co-authored-by: Ramiro Leal-Cavazos <ramiroleal050@gmail.com>
Canonicalization defaulted to replacement when the input dims were from
unknown source. This is obviously incorrect. Tweaked and included test
to prevent future issue.
This patch generalizes tensor.expand_shape and memref.expand_shape to
consume the output shape as a list of SSA values. This enables us to
implement generic reshape operations with dynamic shapes using
collapse_shape/expand_shape pairs.
The output_shape input to expand_shape follows the static/dynamic
representation that's also used in `tensor.extract_slice`.
Differential Revision: https://reviews.llvm.org/D140821
Co-authored-by: Ramiro Leal-Cavazos <ramiroleal050@gmail.com>
If `tensor.reshape` occurs with `d0, d1, d2, ...` for the dimensions we
know that the reshape is a no-op. Checking for this case lets us fold
away the computation.
This fixes an "infinite" loop bug, where the incoming IR was repeatedly
rewritten while adding identical cast operations. The test for
compatible types should include the notion of an encoding. If it
differs, then a
naive fusion into the consumer is invalid.
The current canonicalization of `memref.dim` operating on the result of
`memref.reshape` into `memref.load` is incorrect as it doesn't check
whether the `index` operand of `memref.dim` dominates the source
`memref.reshape` op. It always introduces `memref.load` right after
`memref.reshape` to ensure the `memref` is not mutated before the
`memref.load` call. As a result, the following error is observed:
```
$> mlir-opt --canonicalize input.mlir
func.func @reshape_dim(%arg0: memref<*xf32>, %arg1: memref<?xindex>, %arg2: index) -> index {
%c4 = arith.constant 4 : index
%reshape = memref.reshape %arg0(%arg1) : (memref<*xf32>, memref<?xindex>) -> memref<*xf32>
%0 = arith.muli %arg2, %c4 : index
%dim = memref.dim %reshape, %0 : memref<*xf32>
return %dim : index
}
```
results in:
```
dominator.mlir:22:12: error: operand #1 does not dominate this use
%dim = memref.dim %reshape, %0 : memref<*xf32>
^
dominator.mlir:22:12: note: see current operation: %1 = "memref.load"(%arg1, %2) <{nontemporal = false}> : (memref<?xindex>, index) -> index
dominator.mlir:21:10: note: operand defined here (op in the same block)
%0 = arith.muli %arg2, %c4 : index
```
Properly fixing this issue requires a dominator analysis which is
expensive to run within a canonicalization pattern. So, this patch fixes
the canonicalization pattern by being more strict/conservative about the
legality condition in which we perform this canonicalization.
The more general pattern is also added to `tensor.dim`. Since tensors are
immutable we don't need to worry about where to introduce the
`tensor.extract` call after canonicalization.
Before: op verifiers failed if the input and output ranks were the same
(i.e. no expansion or collapse). This behavior requires users of these
shape ops to verify manually that they are not creating identity
versions of these ops every time they build them -- problematic. This PR
removes this strict verification, and introduces folders for the the
identity cases.
The PR also removes the special case handling of rank-0 tensors for
expand_shape and collapse_shape, there doesn't seem to be any reason to
treat them differently.
The revision does not refactor the inferStaticShape for pack and unpack
ops because they can diverge quickly. Because there are more dimensions
can be inferred (i.e., with inner_tile_sizes) if the pack op does not
have padding value.
This is a follow-up of https://github.com/llvm/llvm-project/pull/80848
Previously, the `tensor.pack` verifier detects unconditional runtime
errors only when tile sizes are static. Now, dynamic tiles are
considered and we only require that the input and either corresponding
tile or output size are static to determine if it will unconditionally
produce errors at runtime.
Previously, `InsertSliceOpSourceCastInserter` was incorrectly applied to
a case when tensor types have an encoding attribute attached to them.
The type `newSrcType` was missing that attribute from the old `srcType`,
which made the expression `srcType == newSrcType` false, since
`tensor<2x2xf32, "foo">` is not equal to `tensor<2x2xf32>`. That lead to
an endless back and forth between `InsertSliceOpSourceCastInserter` that
would introduce a cast and `InsertSliceOpCastFolder` that would remove
it right after.
The canonicalization incrementally converts foldable dynamic hi/lo
padding to static hi/lo values. During this canonicalization the
static-fied valued should be removed from the dynamic values.
This commit renames 4 pattern rewriter API functions:
* `updateRootInPlace` -> `modifyOpInPlace`
* `startRootUpdate` -> `startOpModification`
* `finalizeRootUpdate` -> `finalizeOpModification`
* `cancelRootUpdate` -> `cancelOpModification`
The term "root" is a misnomer. The root is the op that a rewrite pattern
matches against
(https://mlir.llvm.org/docs/PatternRewriter/#root-operation-name-optional).
A rewriter must be notified of all in-place op modifications, not just
in-place modifications of the root
(https://mlir.llvm.org/docs/PatternRewriter/#pattern-rewriter). The old
function names were confusing and have contributed to various broken
rewrite patterns.
Note: The new function names use the term "modify" instead of "update"
for consistency with the `RewriterBase::Listener` terminology
(`notifyOperationModified`).
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 feature had been marked as `TODO` in the `tensor.splat`
documentation for a while. This MR includes:
- Support for dynamically shaped tensors in the return type of
`tensor.splat` with the syntax suggested in the `TODO` comment.
- Updated op documentation.
- Bufferization support.
- Updates in op folders affected by the new feature.
- Unit tests for valid/invalid syntax, valid/invalid folding, and
lowering through bufferization.
- Additional op builders resembling those available in `tensor.empty`.
When the concatenated dim is statically sized but the inputs are
dynamically sized, reifyResultShapes must return the static shape. Fixes
the implementation of the interface for tensor.concat in such cases.
The folder for `tensor.extract` is not operating correctly when it is
consuming the result of a `tensor.from_elements` operation.
The existing unit test named `@extract_from_tensor.from_elements_3d` in
`mlir/test/Dialect/Tensor/canonicalize.mlir` seems an attempt to stress
this code. However, this unit tests creates a `tensor.from_elements` op
exclusively from constants, which gets folded away into a single
constant tensor. Therefore, the buggy code was never executed in unit
tests.
I have added a new unit test named
`@extract_from_tensor.from_elements_variable_3d` that makes sure the
`tensor.from_elements` op is not folded away by having its input
operands come directly from function arguments. The original folder code
would have made this test fail.
This bug was notably affecting the lowering of the `tosa.pad` op in the
`tosa-to-tensor` pass, where the generated code is likely to contain a
`tensor.from_elements` + `tensor.extract` op sequence.
Op verifiers should verify only local properties of an op. The dynamic
sizes of a `tensor.generate` op should not be verified. Dynamic sizes
that have a negative constant value should not prevent the
`tensor.generate` op from verifying.
Also share some code between the `tensor.empty` and `tensor.generate`
"dynamic dim -> static dim" canonicalization patterns.
Remove the `invalid-canonicalize.mlir` file and move the test case to
`canonicalize.mlir`. Canonicalization no longer produces IR that does
not verify (and leaves the op as is).
Without folding the result of the initial tensor.dim, the
ReifyResultShapes implementation would be incorrect because it would
return a dynamic shape for a static result shape.
This adds an operation for concatenating ranked tensors along a static
dimension, as well as a decomposition mirroring the existing lowering
from TOSA to Tensor. This offers a convergence point for "input" like
dialects that include various lowerings for concatenation operations,
easing later analysis. In the future, this op can implement the
necessary interfaces for tiling, as well as potentially add conversions
to some kind of linalg and/or memref counterpart.
This patch adds the op, the decomposition, and some basic
folding/canonicalization. Replacing lowerings with the op (such as the
TOSA lowering) will come as a follow up.
See
https://discourse.llvm.org/t/rfc-tensor-add-a-tensor-concatenate-operation/74858
This commit fixes a crash of the canonicalizer when there are slice ops
with offset/size SSA values that have a negative constant value. Such
ops are invalid if they are reachable and their offsets/sizes should not
be folded to static integer values. (But such ops may appear in
non-reachable block.)
This commit fixes#71150.
In #71153, the `memref.subview` canonicalizer crashes due to a negative
`size` being passed as an operand. During `SubViewOp::verify` this
negative `size` is not yet detectable since it is dynamic and only
available after constant folding, which happens during the
canonicalization passes. As discussed in
<https://discourse.llvm.org/t/rfc-more-opfoldresult-and-mixed-indices-in-ops-that-deal-with-shaped-values/72510>,
the verifier should not be extended as it should "only verify local
aspects of an operation".
This patch fixes#71153 by not folding in aforementioned situation.
Also, this patch adds a basic offset and size check in the
`OffsetSizeAndStrideOpInterface` verifier.
Note: only `offset` and `size` are checked because `stride` is allowed
to be negative
(54d81e49e3).
Fixes https://github.com/llvm/llvm-project/issues/60656.
This patch implements a basic fold for various reshape/resize tensor
operations. Specifically, the folding removes tensor reshape/resize ops
when they are applied to a constant tensor. For example, the following
function:
```mlir
func.func @main(%dest : tensor<8x16x8x32xf32>) -> tensor<8x16x8x32xf32> {
%cst = arith.constant dense<1.000000e-01> : tensor<64x128xf32>
%0 = tensor.pack %cst outer_dims_perm = [1, 0] inner_dims_pos = [0, 1]
inner_tiles = [8, 32] into %dest : tensor<64x128xf32> -> tensor<8x16x8x32xf32>
return %0 : tensor<8x16x8x32xf32>
}
```
will be changed into the following with `mlir-opt -canonicalize`:
```mlir
func.func @main(%arg0: tensor<8x16x8x32xf32>) -> tensor<8x16x8x32xf32> {
%cst = arith.constant dense<1.000000e-01> : tensor<8x16x8x32xf32>
return %cst : tensor<8x16x8x32xf32>
}
```
As a side-note, this patch is essentially an extension of
f79f430d4b.
The destination operand of the `tensor.unpack` operation is only needed
to carry shape information. So if the producer of the destination
operand implements the `DestinationStyleOpInterface`, then fold it into
the `tensor.unpack` operation by replacing the destination operand with
the destination for the source.
`scf::ForallOp` has `shared_outs` tensor operands which are used to
insert partial results into in the parallel terminator. The
`scf::ForallOp` returns one tensor for each `shared_out` which then
contains the combined result from all threads. Since the parallel
terminator cannot change the shape of the `shared_out`, ForallOp is a
`DestinationStyleOp` and this patch implements the interface by
declaring the `outputs` operands as `inits` in the language of the DPS
interface.
For this change to work, we need to add an exception to the Pattern that
folds `tensor.cast` Ops into DPS Ops because `scf::Forall` needs special
handling of its `BlockArgument` Type during this folding.
This patch addresses a crash that occurs when negative dynamic sizes are
provided in tensor.emptyOp by adding a check to ensure that dynamic
sizes are non-negative.
Fixes#64064
This enables canonicalization to fold away unnecessary tensor.dim ops
which in turn enables folding away of other operations, as can be seen
in conv_tensors_dynamic where affine.min operations were folded away.
* Move `foldDynamicIndexList` to `DialectUtils` and simplify function.
* Move `OpWithOffsetSizesAndStridesConstantArgumentFolder` to `ViewLikeInterface` and add documentation.
Differential Revision: https://reviews.llvm.org/D156581
In https://reviews.llvm.org/D151611, a check was added to the tensor verifier to
emit an error on negative tensor dimensions. This check allowed for dynamic
dimensions, hence negative dimensions were still able to get through the verifier.
This is a problem in situations such as #60558, where the dynamic dimension is
converted to a static (and possibly negative) dimension by another pass in the
compiler. This patch fixes that by doing another check during the
`StaticTensorGenerate` conversion, and return a failure if the dimension is
negative.
As a side-note, I have to admit that I do not know why returning a failure in
`StaticTensorGenerate` gives a nice "tensor dimensions must be non-negative"
error. I suspect that the verifier runs again when `return failure()` is called,
but I am not sure.
Fixes#60558.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D155728
* Remove duplicate functions. `tensor::getMixedSize` and `tensor::getMixedSizes` should be used.
* Use `tensor::getMixedSize` instead of `createOrFold<tensor::DimOp>`. This is more efficient. `createOrFold` will create an op an immediately try to fold it. In case of a static dimension size, an attribute can be used directly.
Differential Revision: https://reviews.llvm.org/D153332
