The attribute tells the operator to handle symmetric structures for 2D tensors.
By default, the operator assumes the input tensor is not symmetric.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D138230
This patch re-commit D137468 and D137463, which were reverted by mistakes.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D137579
This reverts commit 53d5d3401120f2aa741a73a5a9ba0ce012ca532c.
This is causing a build failure on the windows mlir bot that was previously hidden by another sparse tensor change that caused failures:
https://lab.llvm.org/buildbot/#/builders/13/builds/28006
This prepare a subsequent revision that will generalize
the insertion code generation. Similar to the support lib,
insertions become much easier to perform with some "cursor"
bookkeeping. Note that we, in the long run, could perhaps
avoid storing the "cursor" permanently and use some
retricted-scope solution (alloca?) instead. However,
that puts harder restrictions on insertion-chain operations,
so for now we follow the more straightforward approach.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D136800
The indices for insert/compress were previously provided as
a memref<?xindex> with proper rank, since that matched the
argument for the runtime support libary better. However, with
proper codegen coming, providing the indices as SSA values
is much cleaner. This also brings the sparse_tensor.insert
closer to unification with tensor.insert, planned in the
longer run.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D134404
The new select operation allows filtering of sparse tensors
by conditionally keeping or removing each element. This
can be used to remove negative values or select the upper
triangle of a matrix.
The select op has a single region which operates on a single
value and must return a boolean True to keep or False to drop.
Reviewed by: aartbik
Differential Revision: https://reviews.llvm.org/D133569
This change goes not impact any semantics yet, but it
is in preparation for implementing the unordered and not-unique
properties. Changing lex_insert to insert is a first step.
Reviewed By: Peiming
Differential Revision: https://reviews.llvm.org/D133531
The "sparsification" pass does not need the ability to use runtime values for
the dimension, so the only source for variability would have been user code.
Restricting the dimension to constants simplifies code generation.
Reviewed By: Peiming, wrengr
Differential Revision: https://reviews.llvm.org/D133458
Introduce new sparse_tensor.storage_get/set to access memory that stores the handle of a sparse tensor. The sparse tensor storage are represented as a tuple, these operation will later be eliminated and the tuple will be flattened after sparse tensor codegen
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D133049
This op used to belong to the sparse dialect, but there are use cases for dense bufferization as well. (E.g., when a tensor alloc is returned from a function and should be deallocated at the call site.) This change moves the op to the bufferization dialect, which now has an `alloc_tensor` and a `dealloc_tensor` op.
Differential Revision: https://reviews.llvm.org/D129985
A new sparse_tensor operation allows for
custom reduction code to be injected during
linalg.generic lowering for sparse tensors.
An identity value is provided to indicate
the starting value of the reduction. A single
block region is required to contain the
custom reduce computation.
Reviewed by: aartbik
Differential Revision: https://reviews.llvm.org/D128004
Now that we have an AllocTensorOp (previously InitTensorOp) in the bufferization dialect, the InitOp in the sparse dialect is no longer needed.
Differential Revision: https://reviews.llvm.org/D126180
When the sparse_tensor dialect lowers linalg.generic,
it makes inferences about how the operations should
affect the looping logic. For example, multiplication
is an intersection while addition is a union of two
sparse tensors.
The new binary and unary op separate the looping logic
from the computation by nesting the computation code
inside a block which is merged at the appropriate level
in the lowered looping code.
The binary op can have custom computation code for the
overlap, left, and right sparse overlap regions. The
unary op can have custom computation code for the
present and absent values.
Reviewed by: aartbik
Differential Revision: https://reviews.llvm.org/D121018
Rationale:
Although file I/O is a bit alien to MLIR itself, we provide two convenient ways
for sparse tensor I/O. The input part was already there (behind the swiss army
knife sparse_tensor.new). Now we have a sparse_tensor.out to write out data. As
before, the ops are kept vague and may change in the future. For now this
allows us to compare TACO vs MLIR very easily.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D117850
This revision implements sparse outputs (from scratch) in all cases where
the loops can be reordered with all but one parallel loops outer. If the
inner parallel loop appears inside one or more reductions loops, then an
access pattern expansion is required (aka. workspaces in TACO speak).
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D115091
This revision contains all "sparsification" ops and rewriting necessary to support sparse output tensors when the kernel has no reduction (viz. insertions occur in lexicographic order and are "injective"). This will be later generalized to allow reductions too. Also, this first revision only supports sparse 1-d tensors (viz. vectors) as output in the runtime support library. This will be generalized to n-d tensors shortly. But this way, the revision is kept to a manageable size.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D113705
Rationale:
The currently used trait was demanding that all types are the same
which is not true (since the sparse part may change and the dim sizes
may be relaxed). This revision uses the correct trait and makes the
rank match test explicit in the verify method.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D112576
This revison lifts the artificial restriction on having exact matches between
source and destination type shapes. A static size may become dynamic. We still
reject changing a dynamic size into a static size to avoid the need for a
runtime "assert" on the conversion. This revision also refactors some of the
conversion code to share same-content buffers.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D111915
This is the first step towards supporting general sparse tensors as output
of operations. The init sparse tensor is used to materialize an empty sparse
tensor of given shape and sparsity into a subsequent computation (similar to
the dense tensor init operation counterpart).
Example:
%c = sparse_tensor.init %d1, %d2 : tensor<?x?xf32, #SparseMatrix>
%0 = linalg.matmul
ins(%a, %b: tensor<?x?xf32>, tensor<?x?xf32>)
outs(%c: tensor<?x?xf32, #SparseMatrix>) -> tensor<?x?xf32, #SparseMatrix>
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D111684
Precursor: https://reviews.llvm.org/D110200
Removed redundant ops from the standard dialect that were moved to the
`arith` or `math` dialects.
Renamed all instances of operations in the codebase and in tests.
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D110797
We have several ways to materialize sparse tensors (new and convert) but no explicit operation to release the underlying sparse storage scheme at runtime (other than making an explicit delSparseTensor() library call). To simplify memory management, a sparse_tensor.release operation has been introduced that lowers to the runtime library call while keeping tensors, opague pointers, and memrefs transparent in the initial IR.
*Note* There is obviously some tension between the concept of immutable tensors and memory management methods. This tension is addressed by simply stating that after the "release" call, no further memref related operations are allowed on the tensor value. We expect the design to evolve over time, however, and arrive at a more satisfactory view of tensors and buffers eventually.
Bug:
http://llvm.org/pr52046
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D111099
Introduces a conversion from one (sparse) tensor type to another
(sparse) tensor type. See the operation doc for details. Actual
codegen for all cases is still TBD.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D107205
The order of testing in two sparse tensor ops was incorrect,
which could cause an invalid cast (crashing the compiler instead
of reporting the error). This revision fixes that bug.
Reviewed By: gussmith23
Differential Revision: https://reviews.llvm.org/D106841
Slowly we are moving toward full support of sparse tensor *outputs*. First
step was support for all-dense annotated "sparse" tensors. This step adds
support for truly sparse tensors, but only for operations in which the values
of a tensor change, but not the nonzero structure (this was refered to as
"simply dynamic" in the [Bik96] thesis).
Some background text was posted on discourse:
https://llvm.discourse.group/t/sparse-tensors-in-mlir/3389/25
Reviewed By: gussmith23
Differential Revision: https://reviews.llvm.org/D104577
This is a very careful start with alllowing sparse tensors at the
left-hand-side of tensor index expressions (viz. sparse output).
Note that there is a subtle difference between non-annotated tensors
(dense, remain n-dim, handled by classic bufferization) and all-dense
annotated "sparse" tensors (linearized to 1-dim without overhead
storage, bufferized by sparse compiler, backed by runtime support library).
This revision gently introduces some new IR to facilitate annotated outputs,
to be generalized to truly sparse tensors in the future.
Reviewed By: gussmith23, bixia
Differential Revision: https://reviews.llvm.org/D104074
A very elaborate, but also very fun revision because all
puzzle pieces are finally "falling in place".
1. replaces lingalg annotations + flags with proper sparse tensor types
2. add rigorous verification on sparse tensor type and sparse primitives
3. removes glue and clutter on opaque pointers in favor of sparse tensor types
4. migrates all tests to use sparse tensor types
NOTE: next CL will remove *all* obsoleted sparse code in Linalg
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D102095
