spell check

Author: oulgen

recipes_source/torch_compile_caching_configuration_tutorial.rst

@@ -24,7 +24,7 @@ Before starting this recipe, make sure that you have the following:
 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.
+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 Inductor’s config file.
@@ -37,17 +37,17 @@ TORCHINDUCTOR_AUTOGRAD_CACHE
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 This setting extends FXGraphCache to store cached results at the AOTAutograd level, instead of at the Inductor level. ``1`` enables, and 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_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 subdirectory.
+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:
+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``)
@@ -56,15 +56,15 @@ Note that if Inductor locates a remote cache entry, it stores the compiled artif
 
 TORCHINDUCTOR_AUTOGRAD_REMOTE_CACHE
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-Like TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE, this setting enables the remote AOT AutogradCache 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:
+Like ``TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE``, this setting enables the remote ``AOTAutogradCache`` 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``)
 
-`TORCHINDUCTOR_AUTOGRAD_REMOTE_CACHE`` depends on `TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE` to be enabled to work. The same Redis server can store both AOTAutograd and FXGraph cache results.
+`TORCHINDUCTOR_AUTOGRAD_REMOTE_CACHE`` depends on ``TORCHINDUCTOR_FX_GRAPH_REMOTE_CACHE`` to be enabled to work. The same Redis server can store both AOTAutograd and FXGraph cache results.
 
 TORCHINDUCTOR_AUTOTUNE_REMOTE_CACHE
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-This setting enables a remote cache for Inductor’s autotuner. As with the remote FX graph cache, the current implementation uses Redis. ``1`` enables caching, and any other value disables it. The same host / port environment variables listed above apply to this cache.
+This setting enables a remote cache for ``TorchInductor``’s autotuner. As with the remote FX graph cache, the current implementation uses Redis. ``1`` enables caching, and any other value disables it. The same host / port environment variables listed above apply to this cache.
 
 TORCHINDUCTOR_FORCE_DISABLE_CACHES
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

recipes_source/torch_compile_caching_tutorial.rst

@@ -30,22 +30,22 @@ Caching Offerings
 
 ``torch.compile`` provides the following caching offerings:
 
-* End to end caching (also known as Mega-Cache)
-* Modular caching of TorchDynamo, TorchInductor, and Triton
+* End to end caching (also known as ``Mega-Cache``)
+* Modular caching of ``TorchDynamo``, ``TorchInductor``, and ``Triton``
 
 It is important to note that caching validates that the cache artifacts are used with the same PyTorch and Triton version, as well as, same GPU when device is set to be cuda.
 
-``torch.compile`` end-to-end caching (a.k.a. Mega-Cache)
+``torch.compile`` end-to-end caching (a.k.a. ``Mega-Cache``)
 ------------------------------------------------------------
 
 End to end caching, from here onwards referred to Mega-Cache, is the ideal solution for users looking for a portable caching solution that can be stored in a database and can later be fetched possibly on a separate machine.
 
-Mega-Cache provides two compiler APIs
+``Mega-Cache`` provides two compiler APIs
 
 * ``torch.compiler.save_cache_artifacts()``
 * ``torch.compiler.load_cache_artifacts()``
 
-The intented use case is after compiling and executing a model, the user calls ``torch.compiler.save_cache_artifacts()`` which will return the compiler artifacts in a portable form. Later, potentially on a different machine, the user may call ``torch.compiler.load_cache_artifacts()`` with these artifacts to prepopulate the ``torch.compile`` caches in order to jump-start their cache.
+The intended use case is after compiling and executing a model, the user calls ``torch.compiler.save_cache_artifacts()`` which will return the compiler artifacts in a portable form. Later, potentially on a different machine, the user may call ``torch.compiler.load_cache_artifacts()`` with these artifacts to pre-populate the ``torch.compile`` caches in order to jump-start their cache.
 
 An example to this is as follows. First, compile and save the cache artifacts.
 
@@ -74,27 +74,27 @@ Later, the user can jump-start their cache by the following.
 
     torch.compiler.load_cache_artifacts(artifact_bytes)
 
-This operation populates all the modular caches that will be discussed in the next section, including PGO, AOTAutograd, Inductor, Triton, and Autotuning.
+This operation populates all the modular caches that will be discussed in the next section, including ``PGO``, ``AOTAutograd``, ``Inductor``, ``Triton``, and ``Autotuning``.
 
 
-Modular caching of TorchDynamo, TorchInductor, and Triton
+Modular caching of ``TorchDynamo``, ``TorchInductor``, and ``Triton``
 -----------------------------------------------------------
 
-The above described MegaCache is also compromised of individual components that can be used without any user intervention. By default, PyTorch Compiler comes with local on-disk caches for TorchDynamo, TorchInductor, and Triton. These caches are as following.
+The above described MegaCache is also compromised of individual components that can be used without any user intervention. By default, PyTorch Compiler comes with local on-disk caches for ``TorchDynamo``, ``TorchInductor``, and ``Triton``. These caches are as following.
 
-* FXGraphCache: cache of graph-based IR components used in compilation
-* Triton Cache: cache of Triton-compilation results (cubin files generated by Triton as well as other caching artifacts)
-* InductorCache: bundling of FXGraphCache and Triton cache
-* AOTAutogradCache: caching of joint graph artifacts
-* PGO-cache: cache of dynamic shape decisions to reduce number of recompilations
+* ``FXGraphCache``: cache of graph-based IR components used in compilation
+* ``Triton Cache``: cache of Triton-compilation results (``cubin`` files generated by ``Triton`` as well as other caching artifacts)
+* ``InductorCache``: bundling of ``FXGraphCache`` and ``Triton`` cache
+* ``AOTAutogradCache``: caching of joint graph artifacts
+* ``PGO-cache``: cache of dynamic shape decisions to reduce number of recompilations
 
 All these cache artifacts are written to ``TORCHINDUCTOR_CACHE_DIR`` which by default will look like ``/tmp/torchinductor_myusername``.
 
 
 Remote Caching
 ----------------
 
-We also provide a remote caching option for users who would like to take advantage of a Redis based cache. Check out `Compile Time Caching Configurations <https://pytorch.org/tutorials/recipes/torch_compile_caching_configuration_tutorial.html>` to learn more about how to enable the Redis based caching.
+We also provide a remote caching option for users who would like to take advantage of a ``Redis`` based cache. Check out `Compile Time Caching Configurations <https://pytorch.org/tutorials/recipes/torch_compile_caching_configuration_tutorial.html>` to learn more about how to enable the ``Redis`` based caching.
 
 
 Conclusion