torch.compiler.config

This is the top-level configuration module for the compiler, containing cross-cutting configuration options that affect all parts of the compiler stack.

You may also be interested in the per-component configuration modules, which contain configuration options that affect only a specific part of the compiler:

• torch._dynamo.config

• torch._inductor.config

• torch._functorch.config

• torch.fx.experimental.config

torch.compiler.config.job_id: Optional[str] = None

Semantically, this should be an identifier that uniquely identifies, e.g., a training job. You might have multiple attempts of the same job, e.g., if it was preempted or needed to be restarted, but each attempt should be running substantially the same workload with the same distributed topology. You can set this by environment variable with TORCH_COMPILE_JOB_ID.

Operationally, this controls the effect of profile-guided optimization related persistent state. PGO state can affect how we perform compilation across multiple invocations of PyTorch, e.g., the first time you run your program we may compile twice as we discover what inputs are dynamic, and then PGO will save this state so subsequent invocations only need to compile once, because they remember it is dynamic. This profile information, however, is sensitive to what workload you are running, so we require you to tell us that two jobs are related (i.e., are the same workload) before we are willing to reuse this information. Notably, PGO does nothing (even if explicitly enabled) unless a valid job_id is available. In some situations, PyTorch can configured to automatically compute a job_id based on the environment it is running in.

Profiles are always collected on a per rank basis, so different ranks may have different profiles. If you know your workload is truly SPMD, you can run with torch._dynamo.config.enable_compiler_collectives to ensure nodes get consistent profiles across all ranks.