This is an automated email from the ASF dual-hosted git repository. tqchen pushed a commit to branch script/canonical-parser-df in repository https://gitbox.apache.org/repos/asf/tvm.git
commit 3743d4ec0c42528f2fb4f740893636a3ebc8d6ec Author: Tianqi Chen <[email protected]> AuthorDate: Tue Sep 22 23:13:51 2026 +0000 [FR] Match native comparison promotion in frontend reference IR --- tests/python/relax/test_frontend_nn_op.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/python/relax/test_frontend_nn_op.py b/tests/python/relax/test_frontend_nn_op.py index ef313b95b5..eb12e25921 100644 --- a/tests/python/relax/test_frontend_nn_op.py +++ b/tests/python/relax/test_frontend_nn_op.py @@ -1172,14 +1172,14 @@ def test_renormalize_top_p_top_k_prob(): v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1]) T.reads(cumsum_sorted[v_ax0, T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)):T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + (T.max(T.max(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + T.int64(1) - T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)))], top_p[v_ax0, 0], top_k[v_ax0, 0], sorted_prob[v_ax0, T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)):T.min(T.min(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + (T.max(T.max(T.int64(0), v_ax1), v_ax1 + T.int64(1)) + [...] T.writes(cutoff[v_ax0, 0]) - if (cumsum_sorted[v_ax0, 0] < top_p[v_ax0, 0] and top_k[v_ax0, 0] > T.int64(1)) == T.bool(False): + if T.Cast("int32", cumsum_sorted[v_ax0, 0] < top_p[v_ax0, 0] and top_k[v_ax0, 0] > T.int64(1)) == 0: cutoff[v_ax0, 0] = sorted_prob[v_ax0, 0] else: - if (cumsum_sorted[v_ax0, v_ax1] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) < top_k[v_ax0, 0]) == T.bool(True): + if T.Cast("int32", cumsum_sorted[v_ax0, v_ax1] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) < top_k[v_ax0, 0]) == 1: if v_ax1 + T.int64(1) == vocab_size: cutoff[v_ax0, 0] = sorted_prob[v_ax0, v_ax1] else: - if (cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) + T.int64(1) < top_k[v_ax0, 0]) == T.bool(False): + if T.Cast("int32", cumsum_sorted[v_ax0, v_ax1 + T.int64(1)] < top_p[v_ax0, 0] and v_ax1 + T.int64(1) + T.int64(1) < top_k[v_ax0, 0]) == 0: cutoff[v_ax0, 0] = sorted_prob[v_ax0, v_ax1 + T.int64(1)] @R.function
