[WIP] Use numpy.ndarray and numpy dtypes instead of memref types in Python API

[hunhoffe]

Summary

This PR optionally allows a Python user to use numpy ndarray and numpy dtype types instead of memref and MLIR types in their designs.

Specifically, changes in this PR include:

• Porting all programming_examples to use numpy types (except those examples using bfloat16 as numpy doesn't support that type directly - those examples use the existing memref/MLIR types without modification)
• A new example, programming_examples/basic/passthrough_pykernel, which uses a mixture of types (to make sure a mix works ok) and is also the only example (that I know of) that shows how to create/call a function defined in Python

If these changes are accepted, I'll do the work to change the programming_guide to use the numpy types, as well as port at least half of the python tests in test to similarly use numpy types. I can include this in a follow-on PR or add it to this PR, depending on advice from reviewers.

Example

For example, to define a memref type in the past (such as for use in initializing an object FIFO) as well as external function argument types, you would write something like:

        # define types
        memRef_ty = T.memref(lineWidthInBytes, T.ui8())

        passThroughLine = external_func(
            "passThroughLine", inputs=[memRef_ty, memRef_ty, T.i32()]
        )

Now, you can instead write a type definition like so:

        line_ty = np.ndarray[(lineWidthInBytes,), np.dtype[np.int8]]

        passThroughLine = external_func(
            "passThroughLine", inputs=[line_ty, line_ty, np.int32]
        )

Requirements

• Python 3.10 or higher for type annotations with bracket notation
• numpy 2.1 or higher for shape annotations for numpy.ndarray type

Internally, some of the code would look better if we bumped all the way to python 3.12+ so we could use type annotations with generics with bracket notation, but so far that is not necessary.

[github-actions]

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Created: 2024-10-01 17:55

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