Fill library node

[tbennun]

Filling an entire data container (like an array) with a single value is a common use case in DaCe codes. Instead of manually specifying how a container should be filled, we would like to have a Library Node that specifies that an array should be filled with a certain value. This is also a performant feature because it can be specialized. Examples:

• Filling an array with a zero value (or any integer) can be lowered to a memset call, or {cuda,hip}Memset for GPUs
• Tensor/Matrix Core memory on GPUs can use wmma::fill_fragment

The library node should be implemented with one output leading to the data container to be filled, and an optional input connector called value which can specify a runtime value to fill. In case the value is constant or symbolic, the input may exist as a property. This is similar to how alpha/beta values are treated in matrix multiplication library nodes.

The library node will live in the DaCe Standard library, similarly to other basic operations such as Reduce. To help with the implementation, refer to the einsum library node. To see how input/output connectors can be defined, refer to TensorTranspose.

The implementation tasks are as follows:

1. Implement the Fill library node in the Standard library
2. Implement a native expansion that uses only SDFG components under the 'pure' expansion
3. (optional) Implement fast specializations
4. Create Fill library nodes in the Python frontend instead of the manual implementation (replacing the implementation of numpy.full)