Vectorize all scalar symbols in vector expressions

Pystencils fails to vectorize very simple kernels:

import numpy as np

import pystencils as ps
from pystencils.astnodes import SympyAssignment, TypedSymbol

f = ps.fields("f: [1D]")
x = TypedSymbol("x", np.float64)

kernel = ps.create_kernel(
    [SympyAssignment(x, 2.0), SympyAssignment(f[0], x)],
    cpu_vectorize_info={"assume_inner_stride_one": True},
)
ps.show_code(kernel)

This example throws an exception in show_code, complaining that the printer can not vectorize type casts.

The problem is that x = 2.0 is moved out of the loop (since it is constant). What remains in the loop is f[i] = x. While the left-hand-side of this expression is vectorized, the right-hand-side is left scalar, leading to the exception.

The issue comes from the insert_vector_casts function. It traverses each expression from the leafs to the root, leaving scalars scalar ¹ and collating mixed expressions to vectors. However, it handles the rhs of assignments separate from the lhs, leading to above issue. Moreover, expressions like a (vec) + (b (scalar) * c (scalar)) are converted to a (vec) + CastToVec(b (scalar) * c (scalar)), which leads to the same exception. The correct way is to directly cast b and c to vectors, not their product. Therefore, insert_vector_casts must know beforehand, whether an expression appears inside a vectorized expression.

This MR fixes that for SympyAssignments. To that end, it first checks whether either side contains a vectorized expression, and if so, casts all symbols to vectors.

Since I am not really sure how to handle the cases for VectorMemoryAccess (line 370/386) and ast.Conditional (line 374/390), I left those untouched.

1. The exception is that CastFunctions are always replaced by vector casts. I do not know whether this is intentional. ↩