Finite-difference methods based on high-order stencils are widely used in seismic simulations, weather forecasting, and computational fluid dynamics. Recently, multiple research groups have begun exploring the use of dataflow architectures, such as Cerebras’ wafer-scale engine, to accelerate stencil computations. However, implementations of stencil computations for dataflow architectures must address unique challenges, such as managing the routing of data communications and accommodating a significantly constrained memory footprint. These make hand-crafting code for a dataflow architecture difficult and time-consuming. This paper describes a framework for developing portable, high-performance implementations of stencil computations for modern node architectures. The paper focuses on code generation strategies for the Cerebras wafer-scale engine, including code generation of router configurations and sequencing of communication for high-order stencils. A 25-point starshaped stencil written using our tool is $7 \times$ shorter than handcrafted code written in Cerebras Software Language (CSL), and it delivers comparable performance to manually written code.