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This paper introduces benchmarks for natural language to MLIR code generation across multiple dialects and a schema-derived constrained decoding stack that allows a small language model to match or exceed large code LMs on structural verifier tasks, without retraining.
This paper presents an MLIR-based compilation method for large language models, using two custom dialects (TopOp and TpuOp) to lower models from framework-agnostic semantics to hardware-specific instructions. It also introduces a three-stage static compilation for autoregressive inference stages: prefill, prefill_kv, and decode.