Files
clang-p2996/mlir/test/Integration/Dialect/SparseTensor/taco
Matthias Springer 6232a8f3d6 [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
2022-06-02 00:03:52 +02:00
..
2022-02-09 16:58:25 -08:00
2022-01-21 15:18:28 -08:00
2022-01-21 08:38:36 -08:00

MLIR-PyTACO: Implementing PyTACO with MLIR

TACO (http://tensor-compiler.org/) is a tensor algebra compiler. TACO defines PyTACO, a domain specific language in Python, for writing tensor algebra applications.

This directory contains the implementation of PyTACO using MLIR. In particular, we implement a Python layer that accepts the PyTACO language, generates MLIR linalg.generic OPs with sparse tensor annotation to represent the tensor computation, and invokes the MLIR sparse tensor code generator (https://mlir.llvm.org/docs/Dialects/SparseTensorOps/) as well as other MLIR compilation passes to generate an executable. Then, we invoke the MLIR execution engine to execute the program and pass the result back to the Python layer.

As can be seen from the tests in this directory, in order to port a PyTACO program to MLIR-PyTACO, we basically only need to replace this line that imports PyTACO:

import pytaco as pt

with this line to import MLIR-PyTACO:

from tools import mlir_pytaco_api as pt