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
The former is redundant because the later carries it as part of
its builder. Add a getContext() helper method to DialectAsmParser
to make this more convenient, and stop passing the context around
explicitly. This simplifies ODS generated parser hooks for attrs
and types.
This resolves PR51985
Recommit 4b32f8bac4 after fixing a dependency.
Differential Revision: https://reviews.llvm.org/D110796
The former is redundant because the later carries it as part of
its builder. Add a getContext() helper method to DialectAsmParser
to make this more convenient, and stop passing the context around
explicitly. This simplifies ODS generated parser hooks for attrs
and types.
This resolves PR51985
Differential Revision: https://reviews.llvm.org/D110796
Rationale:
Passing in a pointer to the memref data in order to implement the
dense to sparse conversion was a bit too low-level. This revision
improves upon that approach with a cleaner solution of generating
a loop nest in MLIR code itself that prepares the COO object before
passing it to our "swiss army knife" setup. This is much more
intuitive *and* now also allows for dynamic shapes.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D108491
Implements lowering dense to sparse conversion, for static tensor types only.
First step towards general sparse_tensor.convert support.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D107681
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
* Previously, we were only generating .h.inc files. We foresee the need to also generate implementations and this is a step towards that.
* Discussed in https://llvm.discourse.group/t/generating-cpp-inc-files-for-dialects/3732/2
* Deviates from the discussion above by generating a default constructor in the .cpp.inc file (and adding a tablegen bit that disables this in case if this is user provided).
* Generating the destructor started as a way to flush out the missing includes (produces a link error), but it is a strict improvement on its own that is worth doing (i.e. by emitting key methods in the .cpp file, we root vtables in one translation unit, which is a non-controversial improvement).
Differential Revision: https://reviews.llvm.org/D105070
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
(1) migrates the encoding from TensorDialect into the new SparseTensorDialect
(2) replaces dictionary-based storage and builders with struct-like data
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D101669
This is the very first step toward removing the glue and clutter from linalg and
replace it with proper sparse tensor types. This revision migrates the LinalgSparseOps
into SparseTensorOps of a sparse tensor dialect. This also provides a new home for
sparse tensor related transformation.
NOTE: the actual replacement with sparse tensor types (and removal of linalg glue/clutter)
will follow but I am trying to keep the amount of changes per revision manageable.
Differential Revision: https://reviews.llvm.org/D101573
This is the very first step toward removing the glue and clutter from linalg and
replace it with proper sparse tensor types. This revision migrates the LinalgSparseOps
into SparseTensorOps of a sparse tensor dialect. This also provides a new home for
sparse tensor related transformation.
NOTE: the actual replacement with sparse tensor types (and removal of linalg glue/clutter)
will follow but I am trying to keep the amount of changes per revision manageable.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D101488
Aart Bik