Compile Time Caching Configuration

Authors: Oguz Ulgen and Sam Larsen

Introduction

PyTorch Compiler implements several caches to reduce compilation latency. This recipe demonstrates how you can configure various parts of the caching in torch.compile.

Prerequisites

Before starting this recipe, make sure that you have the following:

• Basic understanding of torch.compile. See:

  • torch.compiler API documentation

  • Introduction to torch.compile

  • Compile Time Caching in torch.compile

• PyTorch 2.4 or later

Inductor Cache Settings

Most of these caches are in-memory, only used within the same process, and are transparent to the user. An exception is caches that store compiled FX graphs (FXGraphCache, AOTAutogradCache). These caches allow Inductor to avoid recompilation across process boundaries when it encounters the same graph with the same Tensor input shapes (and the same configuration). The default implementation stores compiled artifacts in the system temp directory. An optional feature also supports sharing those artifacts within a cluster by storing them in a Redis database.

There are a few settings relevant to caching and to FX graph caching in particular. The settings are accessible via environment variables listed below or can be hard-coded in the Inductor’s config file.

TORCHINDUCTOR_FX_GRAPH_CACHE

This setting enables the local FX graph cache feature, which stores artifacts in the host’s temp directory. Setting it to 1 enables the feature while any other value disables it. By default, the disk location is per username, but users can enable sharing across usernames by specifying TORCHINDUCTOR_CACHE_DIR (below).

TORCHINDUCTOR_AUTOGRAD_CACHE

This setting extends FXGraphCache to store cached results at the AOTAutograd level, rather than at the Inductor level. Setting it to 1 enables this feature, while any other value disables it. By default, the disk location is per username, but users can enable sharing across usernames by specifying TORCHINDUCTOR_CACHE_DIR (below). TORCHINDUCTOR_AUTOGRAD_CACHE requires TORCHINDUCTOR_FX_GRAPH_CACHE to work. The same cache dir stores cache entries for AOTAutogradCache (under {TORCHINDUCTOR_CACHE_DIR}/aotautograd) and FXGraphCache (under {TORCHINDUCTOR_CACHE_DIR}/fxgraph).

TORCHINDUCTOR_CACHE_DIR

This setting specifies the location of all on-disk caches. By default, the location is in the system temp directory under torchinductor_<username>, for example, /tmp/torchinductor_myusername.

Note that if TRITON_CACHE_DIR is not set in the environment, Inductor sets the Triton cache directory to this same temp location, under the Triton sub-directory.

TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE

This setting enables the remote FX graph cache feature. The current implementation uses Redis. 1 enables caching, and any other value disables it. The following environment variables configure the host and port of the Redis server:

TORCHINDUCTOR_REDIS_HOST (defaults to localhost) TORCHINDUCTOR_REDIS_PORT (defaults to 6379)

Note

Note that if Inductor locates a remote cache entry, it stores the compiled artifact in the local on-disk cache; that local artifact would be served on subsequent runs on the same machine.

TORCHINDUCTOR_AUTOGRAD_REMOTE_CACHE

Similar to TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE, this setting enables the remote AOTAutogradCache feature. The current implementation uses Redis. Setting it to 1 enables caching, while any other value disables it. The following environment variables are used to configure the host and port of the Redis server: * TORCHINDUCTOR_REDIS_HOST (defaults to localhost) * TORCHINDUCTOR_REDIS_PORT (defaults to 6379)

TORCHINDUCTOR_AUTOGRAD_REMOTE_CACHE` requires TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE to be enabled in order to function. The same Redis server can be used to store both AOTAutograd and FXGraph cache results.

TORCHINDUCTOR_AUTOTUNE_REMOTE_CACHE

This setting enables a remote cache for TorchInductor’s autotuner. Similar to remote FX graph cache, the current implementation uses Redis. Setting it to 1 enables caching, while any other value disables it. The same host / port environment variables mentioned above apply to this cache.

TORCHINDUCTOR_FORCE_DISABLE_CACHES

Set this value to 1 to disable all Inductor caching. This setting is useful for tasks like experimenting with cold-start compile times or forcing recompilation for debugging purposes.

Conclusion

In this recipe, we have learned how to configure PyTorch Compiler’s caching mechanisms. Additionally, we explored the various settings and environment variables that allow users to configure and optimize these caching features according to their specific needs.