Deep Learning (DL) compilers have been widely utilized to optimize DL models for efficient deployment across various hardware. Due to their vital role in the DL ecosystem, ensuring their reliability is critical. Such model optimizations are often designed to match specific computational graph structures of a model. However, existing DL compiler fuzzing techniques do not generate tests for each optimization aware of its matched graph structures. In this paper, we proposed OATest, a novel technique that synthesizes optimization-aware tests by extracting patterns from documented tests and integrating them into diverse computational graph contexts. To address the key technical challenge of synthesizing valid and optimization-aware computational graphs, OATest introduces two synthesis strategies: (1) reusing compatible inputs/outputs from existing nodes and (2) creating new nodes with compatible inputs/outputs, establishing effective connections between patterns and contexts. OATest is evaluated on two popular DL compilers, TVM and ONNXRuntime, regarding the bugs revealed by crashes and inconsistencies. The experimental results show that OATest significantly outperforms the state-of-the-art DL compiler fuzzing techniques by detecting more bugs and covering more optimization code in TVM and ONNXRuntime. Particularly, OATest uncovers 56 previously unknown bugs, 42/24 of which have been confirmed/fixed by developers.