[mlir][sparse][NFC] Update remaining test cases

No more to_memref, memref.alloc or memref.dealloc when possible.

Differential Revision: https://reviews.llvm.org/D130023

[mlir][bufferization][NFC] Move sparse_tensor.release to bufferization dialect

This op used to belong to the sparse dialect, but there are use cases for dense bufferization as well. (E.g., when a te

[mlir][bufferization][NFC] Move sparse_tensor.release to bufferization dialect

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

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[mlir][sparse] Switch to One-Shot Bufferize

This change removes the partial bufferization passes from the sparse compilation pipeline and replaces them with One-Shot Bufferize. One-Shot Analysis (an

[mlir][sparse] Switch to One-Shot Bufferize

This change removes the partial bufferization passes from the sparse compilation pipeline and replaces them with One-Shot Bufferize. One-Shot Analysis (and TensorCopyInsertion) is used to resolve all out-of-place bufferizations, dense and sparse. Dense ops are then bufferized with BufferizableOpInterface. Sparse ops are still bufferized in the Sparsification pass.

Details:
* Dense allocations are automatically deallocated, unless they are yielded from a block. (In that case the alloc would leak.) All test cases are modified accordingly. E.g., some funcs now have an "out" tensor argument that is returned from the function. (That way, the allocation happens at the call site.)
* Sparse allocations are *not* automatically deallocated. They must be "released" manually. (No change, this will be addressed in a future change.)
* Sparse tensor copies are not supported yet. (Future change)
* Sparsification no longer has to consider inplacability. If necessary, allocations and/or copies are inserted during TensorCopyInsertion. All tensors are inplaceable by the time Sparsification is running. Instead of marking a tensor as "not inplaceable", it can be marked as "not writable", which will trigger an allocation and/or copy during TensorCopyInsertion.

Differential Revision: https://reviews.llvm.org/D129356

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[mlir][sparse][NFC] Switch InitOp to bufferization::AllocTensorOp

Now that we have an AllocTensorOp (previously InitTensorOp) in the bufferization dialect, the InitOp in the sparse dialect is no lon

[mlir][sparse][NFC] Switch InitOp to bufferization::AllocTensorOp

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

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[mlir][sparse] Add lowering for unary and binary ops

Adding lowering for Unary and Binary required several changes due to
their unique nature of containing custom code for different "regions"
of the

[mlir][sparse] Add lowering for unary and binary ops

Adding lowering for Unary and Binary required several changes due to
their unique nature of containing custom code for different "regions"
of the sparse structure being operated on. Along with a Kind, a pointer
to the Operation is passed along to be merged once the lattice
structure is figured out.

The original operation is maintained, as it is required for subsequent
lattice decisions. However, sparse_tensor.binary has some branches
are considered as fully handled and therefore are marked with as
kBinaryBranch to distinguish them.

A unique aspect of the custom code is that sometimes the desired result
is no result at all -- i.e. a user wants overlapping sparse entries to
become empty in the output. The solution to this is to return an
uninitialized Value(), which is checked and handled elsewhere in the
code and results in nothing being written to the output tensor for that
case.

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D123057

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[mlir][NFC] Update textual references of `func` to `func.func` in Integration tests

The special case parsing of `func` operations is being removed.

[mlir][sparse] Adding standard pipeline for tests.

Addresses https://bugs.llvm.org/show_bug.cgi?id=52409 aka https://github.com/llvm/llvm-project/issues/51751

Reviewed By: aartbik, mehdi_amini

Dif

[mlir][sparse] Adding standard pipeline for tests.

Addresses https://bugs.llvm.org/show_bug.cgi?id=52409 aka https://github.com/llvm/llvm-project/issues/51751

Reviewed By: aartbik, mehdi_amini

Differential Revision: https://reviews.llvm.org/D117919

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[mlir][sparse] generalize sparse tensor output implementation

Moves sparse tensor output support forward by generalizing from injective
insertions only to include reductions. This revision accepts t

[mlir][sparse] generalize sparse tensor output implementation

Moves sparse tensor output support forward by generalizing from injective
insertions only to include reductions. This revision accepts the case with all
parallel outer and all reduction inner loops, since that can be handled with
an injective insertion still. Next revision will allow the inner parallel loop
to move inward (but that will require "access pattern expansion" aka "workspace").

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D114399

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[mlir] Move memref.[tensor_load|buffer_cast|clone] to "bufferization" dialect.

https://llvm.discourse.group/t/rfc-dialect-for-bufferization-related-ops/4712

Differential Revision: https://reviews.l

[mlir] Move memref.[tensor_load|buffer_cast|clone] to "bufferization" dialect.

https://llvm.discourse.group/t/rfc-dialect-for-bufferization-related-ops/4712

Differential Revision: https://reviews.llvm.org/D114552

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[mlir][sparse] first version of "truly" dynamic sparse tensors as outputs of kernels

This revision contains all "sparsification" ops and rewriting necessary to support sparse output tensors when the

[mlir][sparse] first version of "truly" dynamic sparse tensors as outputs of kernels

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

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