Data layout is becoming a central ingredient to achieving high performance code. A layout that is organized according to its desired access order can significantly reduce data movement; the computation must be modified accordingly to match data layout order. We present dlcomp, a code generator for tensor contraction computations. It takes as its input a tensor computation in Einstein notation and a data layout description in the sparse polyhedral format. Using a combination of polyhedra scanning and synthesis, it generates code that iterates over the layout of one tensor and matches each nonzero to the corresponding element(s) in other tensors using an optimized find operation. We briefly discuss how this approach can be generalized beyond sparse tensors.