[BUG] CuteDSL compiler hangs when calling copy with mismatched predicates

The compiler hangs with this code, where we call copy with the wrong copy atom / predicates.
The compiler still should error out instead of hanging.

Steps/Code to reproduce bug

import torch

import cutlass
import cutlass.cute as cute
from cutlass.cute.runtime import from_dlpack


@cute.kernel
def hang_kernel(
    gX: cute.Tensor,
    cX: cute.Tensor,  # coordinate tensor
    shape: cute.Shape,
    tv_layout: cute.Layout,
    tiler_mn: cute.Shape,
):
    tidx, _, _ = cute.arch.thread_idx()
    bidx, _, _ = cute.arch.block_idx()
    blkCrd = cX[(None, None), 0]
    blkX = gX[(None, None), 0]
    copy_atom_load_bf16 = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), cutlass.BFloat16)
    copy_atom_load_fp32 = cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), cutlass.Float32)
    thr_copy_bf16 = cute.make_tiled_copy(copy_atom_load_bf16, tv_layout, tiler_mn).get_slice(tidx)
    thr_copy_fp32 = cute.make_tiled_copy(copy_atom_load_fp32, tv_layout, tiler_mn).get_slice(tidx)
    thrFp32 = thr_copy_fp32.partition_S(blkX)
    thrBf16 = thr_copy_bf16.partition_S(blkX)
    frgX = cute.make_fragment_like(thrFp32)
    thrCrd = thr_copy_bf16.partition_S(blkCrd)
    frgPred = cute.make_fragment(frgX.shape, cutlass.Boolean)
    for i in range(cute.size(frgPred)):
        frgPred[i] = cute.elem_less(thrCrd[i], shape)
    cute.copy(copy_atom_load_bf16, thrFp32, frgX, pred=frgPred)
    # Does not hang if we copy without the predicate, or we call copy with copy_atom_load_fp32


@cute.jit
def hang(mX):
    N = mX.shape[-1]
    warpsize = 32
    num_blocks_N = cute.ceil_div(N, warpsize)
    tiler_mn = (1, N)
    tv_layout = cute.make_layout(
        ((warpsize, 1), (1, num_blocks_N)),
        stride=((1, 1), (1, warpsize))
    )
    print(f"[DSL INFO] Input Tensors:")
    print(f"[DSL INFO]   mX = {mX.type}")
    print(f"[DSL INFO] Tiling Parameters:")
    print(f"[DSL INFO]   tiler_mn = {tiler_mn} per thread block")
    print(f"[DSL INFO]   tv_layout = {tv_layout}")

    mX_expanded_layout = cute.prepend(mX.layout, cute.make_layout((tiler_mn[0],), stride=(0,)))
    mX_expanded = cute.make_tensor(mX.iterator, mX_expanded_layout)
    gX = cute.zipped_divide(mX_expanded, tiler_mn)  # ((TileM,TileN),(RestM,RestN))
    print(f"[DSL INFO] Tiled Tensors:")
    print(f"[DSL INFO]   gX = {gX.type}")
    shape = (4, N)
    idX = cute.make_identity_tensor(shape)
    cX = cute.zipped_divide(idX, tiler=tiler_mn)
    print(f"[DSL INFO]   coord tensor = {cX.type}")
    hang_kernel(gX, cX, shape, tv_layout, tiler_mn).launch(
        grid=[1, 1, 1],
        block=[cute.size(tv_layout, mode=[0]), 1, 1],
    )


def run(N):
    if not torch.cuda.is_available():
        raise RuntimeError(f"Ampere GPU is required to run this example!")
    print(f"Tensor dimensions: [{N}]")
    x = torch.randn(N, device=torch.device("cuda"), dtype=torch.float32)
    print(f"Input tensor shapes:")
    print(f"x: {x.shape}, dtype: {x.dtype}")
    x_tensor = from_dlpack(x, assumed_align=16)
    print("Compiling kernel with cute.compile ...")
    compiled_func = cute.compile(hang, x_tensor)
    print("Done compiling...")


if __name__ == "__main__":
    run(1024)

Cc @thakkarV

Run the supplied reproducer through the hang entry point with N=1024, focusing on hang_kernel and its cute.copy call with the predicate. Trace the CuteDSL compiler path for mismatched copy atoms and predicates; done means the compiler reports an error instead of hanging, while the unpredicated and matching-atom cases remain unaffected.