Revision (<<< Hide revision tags) (Show revision tags >>>) Date Author Comments
Revision tags: llvmorg-20.1.0, llvmorg-20.1.0-rc3, llvmorg-20.1.0-rc2, llvmorg-20.1.0-rc1, llvmorg-21-init, llvmorg-19.1.7, llvmorg-19.1.6, llvmorg-19.1.5, llvmorg-19.1.4, llvmorg-19.1.3, llvmorg-19.1.2, llvmorg-19.1.1, llvmorg-19.1.0, llvmorg-19.1.0-rc4, llvmorg-19.1.0-rc3, llvmorg-19.1.0-rc2, llvmorg-19.1.0-rc1, llvmorg-20-init, llvmorg-18.1.8, llvmorg-18.1.7, llvmorg-18.1.6, llvmorg-18.1.5, llvmorg-18.1.4, llvmorg-18.1.3, llvmorg-18.1.2, llvmorg-18.1.1, llvmorg-18.1.0, llvmorg-18.1.0-rc4, llvmorg-18.1.0-rc3, llvmorg-18.1.0-rc2, llvmorg-18.1.0-rc1, llvmorg-19-init, llvmorg-17.0.6, llvmorg-17.0.5, llvmorg-17.0.4, llvmorg-17.0.3, llvmorg-17.0.2, llvmorg-17.0.1, llvmorg-17.0.0, llvmorg-17.0.0-rc4, llvmorg-17.0.0-rc3, llvmorg-17.0.0-rc2, llvmorg-17.0.0-rc1, llvmorg-18-init, llvmorg-16.0.6, llvmorg-16.0.5, llvmorg-16.0.4, llvmorg-16.0.3, llvmorg-16.0.2, llvmorg-16.0.1, llvmorg-16.0.0, llvmorg-16.0.0-rc4, llvmorg-16.0.0-rc3, llvmorg-16.0.0-rc2, llvmorg-16.0.0-rc1, llvmorg-17-init, llvmorg-15.0.7, llvmorg-15.0.6, llvmorg-15.0.5, llvmorg-15.0.4, llvmorg-15.0.3, llvmorg-15.0.2, llvmorg-15.0.1, llvmorg-15.0.0, llvmorg-15.0.0-rc3, llvmorg-15.0.0-rc2, llvmorg-15.0.0-rc1, llvmorg-16-init, llvmorg-14.0.6, llvmorg-14.0.5, llvmorg-14.0.4, llvmorg-14.0.3, llvmorg-14.0.2, llvmorg-14.0.1, llvmorg-14.0.0, llvmorg-14.0.0-rc4, llvmorg-14.0.0-rc3, llvmorg-14.0.0-rc2
# ad932a75 11-Feb-2022 Bixia Zheng <[email protected]>

[mlir][sparse][taco] Support true dense tensors and all dense sparse tensors.

The only method to create a true dense tensor (i.e un-annotated) in MLIR-PyTACO
is through the from_array method. Howeve

[mlir][sparse][taco] Support true dense tensors and all dense sparse tensors.

The only method to create a true dense tensor (i.e un-annotated) in MLIR-PyTACO
is through the from_array method. However, the annotated all dense tensors are
also implemented as true dense tensor currently. The PR fixes the
implementation to support annotated all dense sparse tensors.

Extend the tensor init method to support the construction of a tensor without
any sparsity annotation.

Change the tensor to_file method to only support writing unpacked sparse
tensors to file through the MLIR sparse tensor dialect.

Add unit tests for true dense tensors and all dense sparse tensors.

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D119500

show more ...


# 719b865b 10-Feb-2022 Aart Bik <[email protected]>

[mlir][sparse][pytaco] add SDDMM test with two different ways of defining kernel

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D119465


# 6195a254 09-Feb-2022 Aart Bik <[email protected]>

[mlir][sparse][pytaco] test cleanup

removed obsoleted TODO
removed strange Fp precision for coordinates
lined up meta data testing code for readability

Reviewed By: bixia

Differential Revision: ht

[mlir][sparse][pytaco] test cleanup

removed obsoleted TODO
removed strange Fp precision for coordinates
lined up meta data testing code for readability

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D119377

show more ...


Revision tags: llvmorg-14.0.0-rc1
# 61a3dd70 04-Feb-2022 Bixia Zheng <[email protected]>

[mlir][taco] Use sparse_tensor.out to write sparse tensors to files.

Add a Python method, output_sparse_tensor, to use sparse_tensor.out to write
a sparse tensor value to a file.

Modify the method

[mlir][taco] Use sparse_tensor.out to write sparse tensors to files.

Add a Python method, output_sparse_tensor, to use sparse_tensor.out to write
a sparse tensor value to a file.

Modify the method that evaluates a tensor expression to return a pointer of the
MLIR sparse tensor for the result to delay the extraction of the coordinates and
non-zero values.

Implement the Tensor to_file method to evaluate the tensor assignment and write
the result to a file.

Add unit tests. Modify test golden files to reflect the change that TNS outputs
now have a comment line and two meta data lines.

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D118956

show more ...