I tried to write a complicate new op and tune it with auto_scheduler.In the description of op, I use the topi.reshape for several times，and I use topi.conv2d as well.
The new OP works well with auto_scheduler, But when I see the printed schedule, I am confused. > PadInput_i0, PadInput_i1, PadInput_i2, PadInput_i3 = > tuple(PadInput.op.axis) + tuple(PadInput.op.reduce_axis) > T_reshape_ax0, T_reshape_ax1, T_reshape_ax2, T_reshape_ax3 = > tuple(T_reshape.op.axis) + tuple(T_reshape.op.reduce_axis) > pad_temp_i0, pad_temp_i1, pad_temp_i2, pad_temp_i3 = > tuple(pad_temp.op.axis) + tuple(pad_temp.op.reduce_axis) > compute_i, compute_j = tuple(compute.op.axis) + > tuple(compute.op.reduce_axis) > T_reshape_ax0, T_reshape_ax1, T_reshape_ax2, T_reshape_ax3 = > tuple(T_reshape.op.axis) + tuple(T_reshape.op.reduce_axis) > compute_nn, compute_ff, compute_yy, compute_xx, compute_rc, > compute_ry, compute_rx = tuple(compute.op.axis) + > tuple(compute.op.reduce_axis) > compute_red_ax0, compute_red_ax1, compute_red_ax2, compute_red_k1 = > tuple(compute_red.op.axis) + tuple(compute_red.op.reduce_axis) > T_reshape_ax0, T_reshape_ax1, T_reshape_ax2, T_reshape_ax3 = > tuple(T_reshape.op.axis) + tuple(T_reshape.op.reduce_axis) > compute_local, = s.cache_write([compute], "local") > compute_local_nn_c, compute_local_ff_c, compute_local_yy_c, > compute_local_xx_c, compute_local_rc, compute_local_ry, compute_local_rx = > tuple(compute_local.op.axis) + tuple(compute_local.op.reduce_axis) > compute_local_nn_c_o_i, compute_local_nn_c_i = > s[compute_local].split(compute_local_nn_c, factor=3) You can see there are several T_reshape_op, So I cannot distinguish them。So I wanna know can we name the topi op, or is there a way to distinguish the ops. Thank you --- [Visit Topic](https://discuss.tvm.apache.org/t/print-auto-schedule-python-schedule-with-topi-op/11363/1) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/6d21ed1a7439e456e1b643bf98a58626eea7846c93915d374d373a0eb4468af8).
