  An important challenge in Machine Learning compilers like XLA is multi-pass
optimization and analysis. There has been recent interest chiefly in XLA
target-dependent optimization on the graph-level, subgraph-level, and
kernel-level phases. We specifically focus on target-independent optimization
XLA HLO pass ordering: our approach aims at finding the optimal sequence of
compiler optimization passes, which is decoupled from target-dependent
optimization. However, there is little domain specific study in pass ordering
for XLA HLO. To this end, we propose introducing deep Reinforcement Learning
(RL) based search for optimal XLA HLO pass ordering. We also propose
enhancements to the deep RL algorithms to further improve optimal search
performance and open the research direction for domain-specific guidance for
RL. We create an XLA Gym experimentation framework as a tool to enable RL
algorithms to interact with the compiler for passing optimizations and thereby
train agents. Overall, in our experimentation we observe an average of $13.3\%$
improvement in operation count reduction on a benchmark of GPT-2 training
graphs and $10.4\%$ improvement on a diverse benchmark including GPT-2, BERT,
and ResNet graphs using the proposed approach over the compiler's default phase
ordering.
