  Tensor program tuning is essential for the efficient deployment of deep
neural networks. Search-based approaches have demonstrated scalability and
effectiveness in automatically finding high-performance programs for specific
hardware. However, the search process is often inefficient, taking hours or
even days to discover optimal programs due to the exploration mechanisms guided
by an accurate but slow learned cost model. Meanwhile, the learned cost model
trained on one platform cannot seamlessly adapt online to another, which we
call cross-platform online unawareness.
  In this work, we propose Pruner and MoA-Pruner. Pruner is a speculative
exploration mechanism that accelerates the search process using a
"Draft-then-Verify" paradigm. Instead of applying the complex learned cost
model to all explored candidates, Pruner drafts small-scale speculative
candidates by introducing a naive symbol analyzer (draft model), then
identifies the best candidates by the learned cost model. MoA-Pruner introduces
Momentum online Adaptation to address the cross-platform online unawareness.
  We incorporate these techniques into the Ansor and conduct extensive
experiments on three GPU-based platforms. Results show that in online cost
model tuning scenarios, Pruner and MoA-Pruner can achieve an average speedup of
$2.6 \times$ and $4.82 \times$ compared to Ansor. In offline tuning scenarios,
Pruner can achieve an average speedup of $4.75 \times$ and $4.05\times$
compared to TenSet and TLP, respectively. The code is available at
https://github.com/qiaolian9/Pruner.
