ML Compiler IR for GPU Inference Task Mapping and Layouts

The patent introduces a new intermediate representation (IR) that serves as a mediator between high-level Python code and low-level GPU instructions. This IR incorporates tile-based abstractions that automatically handle task mappings and memory layouts, eliminating the need for manual specifications while maintaining fine-grained control. The IR acts as an intermediary layer that translates intuitive high-level operations into optimized low-level code without requiring programmers to directly manage complex GPU architecture details.

The patent segments the compilation process into distinct optimization passes that operate at different levels of abstraction. The new IR introduces tile-based primitives that represent fine-grained computational units, allowing separate optimization passes to target specific aspects of mixed-type tensor operations and low-precision computations. This segmentation enables precise optimizations for specific operation types while maintaining general compiler functionality through the unified IR framework.

The patent changes the parameter representation by introducing a new IR that natively supports mixed-precision data types through tile-based abstractions. Instead of requiring separate handling for different precision types, the IR uses parameter transformations to automatically manage type conversions and optimizations. The tile-based primitives allow the compiler to apply parameter changes at the optimization level, handling mixed input types from weight-only quantization without increasing programming complexity for users.