Drop the `createPadScalarOp` from Utils.h since it is a duplicate of
the `build` method added here.
Differential Revision: https://reviews.llvm.org/D136493
`getDestinationOperands` was almost a duplicate of `DestinationStyleOpInterface::getOutputOperands`. Now that the interface has been moved to mlir/Interfaces, it is no longer needed.
Differential Revision: https://reviews.llvm.org/D136240
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
The assert is misplaced as the result type is allowed to be null. A few
lines below the result type is inferred if it is passed a nullptr.
Besides, this behavior is described in the documentation of the builder.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D136262
These operations have undefined behavior if the index is not less than the rank of the source tensor / memref, so they cannot be freely speculated like they were before this patch. After this patch we speculate them only if we can prove that they don't have UB.
Depends on D135505.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D135748
Inserting a tensor into an equivalent tensor is a no-op after bufferization. No alloc is needed.
Differential Revision: https://reviews.llvm.org/D132662
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 interface is implemented by memref.dim and tensor.dim. This change makes it possible to remove a build dependency of the Affine dialect on the Tensor dialect (and maybe also the MemRef dialect in the future).
Differential Revision: https://reviews.llvm.org/D133595
The utility function should live in `StaticValueUtils.h` as it provides
a convenient way to convert a vector of OpFoldResults into a vector of
Values.
Reviewed By: nicolasvasilache, cota
Differential Revision: https://reviews.llvm.org/D134451
The utility function should live in `StaticValueUtils.h` as it provides
a convenient way to convert a vector of OpFoldResults into a vector of
Values.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D134451
Summary:
Use the new enum in TilingIterface and verify that `iterator_type` attribute in
LinalgOp interface is compatible with the enum values. Later IteratorType enum
will be used in LinalgInterface to replace the current `iterator_type` attribute
array of string.
Existing enums in Linalg are moved into a separate td file and tablegen build
target. This is necessary, have one I32EnumAttr in a shared space that generated
enum class definition and EnumAttrs is dialect-specific location. Otherwise
there might be a conflict that I32EnumAttr generates enum definitions in
multiple places.
Differential Revision: https://reviews.llvm.org/D134634
This reverts commit 730ae80d3e1c47f93f725acb2d37f06fcba06953.
It fails with a linking errors: `undefined reference to
`mlir::getValueOrCreateConstantIndexOp` in `libMLIRDialectUtils`.
The utility function should live in `StaticValueUtils.h` as it provides
a convenient way to convert a vector of OpFoldResults into a vector of
Values.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D134451
So that these utility functions can also be used ViewLikeInterface
ops not in the memref dialect.
Reviewed By: mravishankar, christopherbate
Differential Revision: https://reviews.llvm.org/D134487
This relands commit 5d4603a02d0c3e0106b10d245322b1d2072c0c3d.
It cludes fixes to GCC test failures and simplification to
the implementation.
Co-authored-by: Mahesh Ravishankar <ravishankarm@google.com>
Co-authored-by: Christopher Bate <cbate@nvidia.com>
This function must be implemented for all ops, where the result memref type is different from the input memref type.
Differential Revision: https://reviews.llvm.org/D134331
This reverts commit 5d4603a02d0c3e0106b10d245322b1d2072c0c3d.
The Dialect/Tensor/fold-consecutive-insert-extract-slice.mlir test is
failing when built with GCC
This commit adds utility functions to perform general merging of
OffsetSizeAndStrideOpInterface by supporting producer rank
reducing and non-unit strides.
With it we can extend MergeConsecutiveExtractSlice to support
more cases.
Co-authored-by: Mahesh Ravishankar <ravishankarm@google.com>
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D134294
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
The three following ops in the memref dialect: transpose, expand_shape,
collapse_shape, have been originally designed to operate on memrefs with
strided layouts but had to go through the affine map representation as the type
did not support anything else. Make these ops produce memref values with
StridedLayoutAttr instead now that it is available.
Depends On D133938
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D133947
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 change refines the semantics of scf.foreach_thread. Tensors that are inserted into in the terminator must now be passed to the region explicitly via `shared_outs`. Inside of the body of the op, those tensors are then accessed via block arguments.
The body of a scf.foreach_thread is now treated as a repetitive region. I.e., op dominance can no longer be used in conflict detection when using a value that is defined outside of the body. Such uses may now be considered as conflicts (if there is at least one read and one write in the body), effectively privatizing the tensor. Shared outputs are not privatized when they are used via their corresponding block arguments.
As part of this change, it was also necessary to update the "tiling to scf.foreach_thread", such that the generated tensor.extract_slice ops use the scf.foreach_thread's block arguments. This is implemented by cloning the TilingInterface op inside the scf.foreach_thread, rewriting all of its outputs with block arguments and then calling the tiling implementation. Afterwards, the cloned op is deleted again.
Differential Revision: https://reviews.llvm.org/D133114
`getTiledImplementation`/`generateResultTileValue` only computes the tiled operation, but does not insert the result into any tensor.
Differential Revision: https://reviews.llvm.org/D133015
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.
Reviewed By: mehdi_amini, rriddle
Differential Review: https://reviews.llvm.org/D132838
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.
Reviewed By: mehdi_amini, rriddle
Differential Review: https://reviews.llvm.org/D132838
Even though iter_arg and init_arg of an scf.for loop may have the same tensor type, their bufferized memref types are not necessarily equal. It is sometimes necessary to insert a cast in case of differing layout maps.
Differential Revision: https://reviews.llvm.org/D132860
This change generalizes getBufferType. This function can be used to predict the buffer type of any tensor value (not just BlockArguments) without changing any IR. It also subsumes getMemorySpace. This is useful for loop bufferization, where the precise buffer type of an iter_arg cannot be known without examining the loop body.
Differential Revision: https://reviews.llvm.org/D132859
Update the implementation of `TilingInterface` for `tensor.pad`
operations to allow tiling the op using the existing patterns for the
interface. Verify that tests that pass with existing pad tiling
patterns producer the same results through TilingInterface patterns.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D132720
parallel_insert_slice doesn't return a value therefore we shouldn't try
to fold the result. The insert folding don't apply to this op.
The current folding would cause pattern rewrite to not be able to
converge.
Differential Revision: https://reviews.llvm.org/D132668
tensor.pad is lowered to tensor.generate + tensor.insert_slice during bufferization. For best performance with constant padding values, users should vectorize the IR before bufferizing it.
This change also relaxes tje restriction that no new ops that bufferize to a memory write should be added during bufferization. Since bufferization has been split into two steps a while ago (tensor copy insertion + bufferization), it is reasonable to allow this now.
Differential Revision: https://reviews.llvm.org/D132355
This commit folds a `tensor.cast` op into a `tensor.collapse_shape` op
when following two conditions meet:
1. the `tensor.collapse_shape` op consumes result of the `tensor.cast` op.
2. `tensor.cast` op casts to a more dynamic version of the source tensor.
This is added as a canonicalization pattern in `tensor.collapse_shape` op.
Signed-Off-By: Gaurav Shukla <gaurav@nod-labs.com>
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D130650
InsertSliceOp and ParallelInsertSliceOp are very similar and can share some of the bufferization analysis code.
Differential Revision: https://reviews.llvm.org/D130465
Load dialects that will be generated by the extension. (Except for BufferizationDialect and MemrefDialect which are loaded already.)
Differential Revision: https://reviews.llvm.org/D130463
The `unknownTypeConversion` bufferization option (enum) is now a type converter function option. Some logic of `getMemRefType` is now handled by that function.
This change makes type conversion more controllable. Previously, there were only two options when generating memref types for non-bufferizable ops: Static identity layout or fully dynamic layout. With this change, users of One-Shot Bufferize can provide a function with custom logic.
Differential Revision: https://reviews.llvm.org/D129273
