56d68e8d7a
If `copy` is specified, the newly allocated buffer is initialized with the given contents. Also add an optional `escape` attribute to indicate whether the buffer of the tensor may be returned from the parent block (aka. "escape") after bufferization. This change is in preparation of connecting One-Shot Bufferize to the sparse compiler. Differential Revision: https://reviews.llvm.org/D126570
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