As introduced, Just-In-Time (JIT) compilation fundamentally shifts when optimizations and code generation occur, moving these phases from an offline Ahead-of-Time (AOT) process to the actual runtime execution. This temporal shift imposes unique and demanding requirements on the Intermediate Representation (IR) used within the JIT compiler. Unlike AOT scenarios where compilation time is less critical, the IR design in a JIT system must prioritize not only expressive power but also the speed of its construction, manipulation, and lowering, as these directly impact the user-perceived application latency.
The effectiveness of an ML JIT compiler hinges significantly on the capabilities of its IR. Several properties are particularly important:
A core challenge addressed by JIT IRs is representing information that is initially unknown or variable. Tensor shapes are the canonical example. An AOT compiler might require all tensor dimensions to be static constants. A JIT compiler, however, often encounters tensors where some dimensions depend on runtime inputs.
The IR can handle this using mechanisms like:
tensor<Nx1024xf32>
).C = matmul(A, B)
where A
has shape (M,K) and B
has shape (K,N) would have an IR representation encoding that C
has shape (M,N), regardless of whether M, K, or N are concrete values or symbols.This ability to represent and manipulate partially specified information is fundamental to enabling runtime specialization, where the JIT generates code optimized for the actual tensor shapes encountered during a specific execution trace.
To balance high-level semantics with low-level optimization needs, JIT IRs often adopt a layered or multi-dialect approach. A typical flow within the JIT might look like this:
A view of IR lowering stages within a JIT compiler. Compilation starts from captured framework operations, passes through progressively lower-level IRs enabling different optimizations, and culminates in target code generation. Shape specialization often occurs during the transition from high-level to mid-level IR.
This layered approach allows optimizations to be applied at the most suitable level of abstraction: graph fusion on the high-level IR, loop tiling on the mid-level IR, and instruction scheduling on the low-level IR. The JIT compiler orchestrates the transitions (lowerings) between these layers.
The method used to capture the user's model impacts the initial form of the JIT IR:
tf.function
decorator with autograph), the JIT parses this code directly. The resulting IR often more closely resembles an Abstract Syntax Tree (AST) or includes explicit control-flow structures (like scf.if
or scf.for
in MLIR terminology). This gives the compiler more explicit program structure to analyze compared to tracing.In both cases, the initial IR captures the program structure, which is then refined and optimized using the dynamic context available at runtime.
While sharing foundational concepts with AOT compiler IRs (like SSA form, operation semantics), JIT IRs operate under different constraints. AOT compilers can afford expensive analyses and transformations because compilation time is offline. They often rely on detailed static information about shapes and types.
JIT IRs, conversely, must be:
TensorFlow's XLA uses HLO (High Level Optimizer IR) which is graph-based and suitable for aggressive fusion, while PyTorch's TorchScript uses an IR that retains more Pythonic semantics initially before lowering. Both are designed to balance representational power with the performance demands of JIT compilation, embodying the principles discussed here. These systems are examined in more detail in Sections 7.7 and 7.8.
In summary, the intermediate representation is a foundation of any JIT compilation system for machine learning. Its design must navigate the trade-offs between faithfully representing high-level, potentially dynamic program semantics and enabling efficient, runtime-sensitive optimization and code generation. The ability to handle dynamic information, support multiple levels of abstraction, and facilitate rapid manipulation are defining characteristics of effective JIT IRs.
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