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See https://github.com/llvm/llvm-project/pull/147168 for more info.
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See https://github.com/llvm/llvm-project/pull/147168 for more info.
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The motivation is to avoid having to negate `isDynamic*` checks, avoid
double negations, and allow for `ShapedType::isStaticDim` to be used in
ADT functions without having to wrap it in a lambda performing the
negation.
Also add the new functions to C and Python bindings.
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ArrayRef has a constructor that accepts std::nullopt. This
constructor dates back to the days when we still had llvm::Optional.
Since the use of std::nullopt outside the context of std::optional is
kind of abuse and not intuitive to new comers, I would like to move
away from the constructor and eventually remove it.
One of the common uses of std::nullopt is in one of the constructors
for ValueRange. This patch takes care of the migration where we need
ValueRange() to facilitate perfect forwarding. Note that {} would be
ambiguous for perfecting forwarding to work.
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Also fix some typos in comments
---------
Co-authored-by: Mehdi Amini <joker.eph@gmail.com>
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(#109435)
This PR fixes a bug in `SparseTensorDimOpRewriter` when `tensor.dim` has
an unranked tensor type. To prevent crashes, we now use
`tryGetSparseTensorType` instead of `getSparseTensorType`. Fixes
#107807.
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(#92158)
…ducer.
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1. Remove the trailing comma for the last element of memref and add
closing parenthesis.
2. Change integration tests to use the new format.
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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
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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>
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Reverts llvm/llvm-project#69267
this broke some bots.
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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>
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This change cleans up call sites. Next step is to mark the member
functions deprecated.
See https://mlir.llvm.org/deprecation and
https://discourse.llvm.org/t/preferred-casting-style-going-forward.
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Note that even though the sparse runtime support lib always uses SoA
storage for COO storage (and provides correct codegen by means of views
into this storage), in some rare cases we need the true physical SoA
storage as a coordinate buffer. This PR provides that functionality by
means of a (costly) coordinate buffer call.
Since this is currently only used for testing/debugging by means of the
sparse_tensor.print method, this solution is acceptable. If we ever want
a performing version of this, we should truly support AoS storage of COO
in addition to the SoA used right now.
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(#84987)
…a sparse tensor.
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With an integration test example
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More information is more testing!
Also adjusts already migrated integration tests
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The sparse structure buffers might not always be memrefs with rank == 1
with the presence of batch levels.
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This operation is mainly used for testing and debugging purposes but
provides a very convenient way to quickly inspect the contents of a
sparse tensor (all components over all stored levels).
Example:
[ [ 1, 0, 2, 0, 0, 0, 0, 0 ],
[ 0, 0, 0, 0, 0, 0, 0, 0 ],
[ 0, 0, 0, 0, 0, 0, 0, 0 ],
[ 0, 0, 3, 4, 0, 5, 0, 0 ]
when stored sparse as DCSC prints as
---- Sparse Tensor ----
nse = 5
pos[0] : ( 0, 4, )
crd[0] : ( 0, 2, 3, 5, )
pos[1] : ( 0, 1, 3, 4, 5, )
crd[1] : ( 0, 0, 3, 3, 3, )
values : ( 1, 2, 3, 4, 5, )
----
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When creating a new block in (conversion) rewrite patterns,
`OpBuilder::createBlock` must be used. Otherwise, no
`notifyBlockInserted` notification is sent to the listener.
Note: The dialect conversion relies on listener notifications to keep
track of IR modifications. Creating blocks without the builder API can
lead to memory leaks during rollback.
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*NOTE*: the `SoA` property only makes a difference on codegen path, and
is ignored in libgen path at the moment (only SoA COO is supported).
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LevelFormat and Properties" (#81923) (#81934)
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LevelF…" (#81923)
Reverts llvm/llvm-project#81799 ; this broke the mlir gcc7 bot.
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(#81799)
…ormat and properties instead.
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(NFC)
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sparse tensor level. (#78345)
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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`).
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`hasTensor/BufferSemantics` (#77574)
Rename interface functions as follows:
* `hasTensorSemantics` -> `hasPureTensorSemantics`
* `hasBufferSemantics` -> `hasPureBufferSemantics`
These two functions return "true" if the op has tensor/buffer operands
but not buffer/tensor operands.
Also drop the "ranked" part from the interface, i.e., do not distinguish
between ranked/unranked types.
The new function names describe the functions more accurately. They also
align their semantics with the notion of "tensor semantics" with the
bufferization framework. (An op is supposed to be bufferized if it has
tensor operands, and we don't care if it also has memref operands.)
This change is in preparation of #75273, which adds
`BufferizableOpInterface::hasTensorSemantics`. By renaming the functions
in the `DestinationStyleOpInterface`, we can avoid name clashes between
the two interfaces.
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Separates actual transformation files from supporting utility files in
the transforms directory. Includes a bazel overlay fix for the build (as
well as a bit of cleanup of that file to be less verbose and more
flexible).
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This centralizes all COO methods, and provides a cleaner API. Note that
the "enc" only constructor is a temporary workaround the need for COO
methods inside the "enc" only storage specifier.
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Migrates dangling convenience method into proper SparseTensorType class.
Also cleans up some details (picking right dim2lvl/lvl2dim). Removes
more dead code.
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The "Dim" prefix is a legacy left-over that no longer makes sense, since
we have a very strict "Dimension" vs. "Level" definition for sparse
tensor types and their storage.
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The "dimension" before "level" does not really make sense Note that
renaming the actual type DimLevelType to LevelType is still TBD, since
this is an externally visible change (e.g. visible to Python API).
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This avoids seeing non-perm on the convert from COO to non-COO for
higher dimensional new operators (viz. reading in BSR).
This is step 1 out of 3 to make sparse_tensor.new work for BSR
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When the Powers That Be decided that the name "sparse compiler" should
be changed to "sparsifier", we negected to change some of the comments
in the code; this pull request completes the name change.
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This removes the most obvious ones. The others are still TBD.
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support direct convert from a constant tensor defined by
SparseArrayElements to BSR
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This is a first revision in a small series of changes that removes
duplications between direct encoding methods and sparse tensor type
wrapper methods (in favor of the latter abstraction, since it provides
more safety). The goal is to simply end up with "just" SparseTensorType
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