Do we have plan to introduce step attribute to ForNode?

wrongtest · November 5, 2025, 6:58am

[TIR] Add step attribute to ForNode (Initial codes)

main ← wrongtest-intellif:demo_fornode_step_support

opened 06:55AM - 05 Nov 25 UTC

A prototyping change to add `ForNode::step` It try add minimal codes to run f…ollowing naive test cases. ```python import tvm import numpy as np from tvm.script import tir as T @T.prim_func def function(A: T.Buffer[(1024)], B: T.Buffer[(1024)], C: T.Buffer[(1024)]): for i in range(0, 100, 3): C[i] = A[i] + B[i] print(function) lib = tvm.compile(function, target="c") print(lib.mod.inspect_source()) lib2 = tvm.compile(function, target="llvm") a = np.random.uniform(1, 100, [1024]).astype("float32") b = np.random.uniform(1, 100, [1024]).astype("float32") c = np.zeros([1024]).astype("float32") lib(a, b, c) c[:] = 0 print(c[:]) lib2(a, b, c) print(c[:]) ``` The aspects to check for a real roadmap may be 1. Roundtrip support for TIR tvmscript grammar 2. Correctness of TIR lowering pipeline - For **all transformations and analysis tools**, either it make adaptions to non-consecutive loop iteration indices, or loop canonicalization required. - Ensure the original `ForNode::step` is not dropped by mutations on `ForNode`. 3. Correctness of TensorIR schedule and MetaSchedule - Since many primitives depend on affine bindings. Loop canonicalization is required. 4. CodeGen support - Check mainstream targets could support the loop step. 5. Compatibility issues - Try to argue that the change would not affect existing works, since `ForNode` is an important construction in TVM.

This is a draft for the issue.