Compilers must check the legality of code transformations to guarantee the correctness of applying a sequence of code transformations to a given code. While such a legality check needs to be precisely computed in general, we can use an approximate legality prediction model in certain cases, such as training a reinforcement learning (RL) agent for schedule prediction. In this paper, we propose an approximate method for legality checks. We propose a novel DL model for predicting the legality of transformations. The model takes the code representation and a list of transformations as input and predicts whether applying those transformations to the code is legal. We implement and evaluate the proposed model, demonstrating its effectiveness. Our evaluation shows an F1 score of 0.91 on a test set of randomly generated programs. To further evaluate the model in a practical scenario, we used the model to replace the legality check used during the training of an RL agent designed for automatic code optimization. We demonstrate that such a replacement enables the agent to train on twice as many steps, resulting in faster training and reducing resource usage by approximately 80\% for CPU and 35\% for RAM. The agent trained using this approach maintains comparable performance, with only a 4\% reduction on benchmarks from the Polybench suite compared to the traditional method.

翻译：编译器必须检查代码变换的合法性，以确保对给定代码应用一系列变换的正确性。虽然此类合法性检查通常需要精确计算，但在某些情况下（例如训练用于调度预测的强化学习（RL）代理时），我们可以使用近似的合法性预测模型。本文提出了一种用于合法性检查的近似方法。我们设计了一种新颖的深度学习（DL）模型来预测变换的合法性。该模型以代码表示和变换列表作为输入，预测将这些变换应用于代码是否合法。我们实现并评估了所提出的模型，证明了其有效性。评估结果显示，在随机生成的程序测试集上，模型的F1分数达到0.91。为了在实际场景中进一步评估模型，我们使用该模型替代了为自动代码优化设计的RL代理训练过程中使用的合法性检查。结果表明，这种替代使代理能够以两倍的训练步数进行训练，从而加速训练过程，并将CPU资源使用量减少约80%，RAM使用量减少约35。采用此方法训练的代理保持了可比性能，在Polybench基准测试套件上仅比传统方法降低4%。