This PR adds a new config option, `caching_precompile`, and a `DynamoCache`, which loads and saves Dynamo Cache entries automatically. It also hooks up DynamoCache to PrecompileContext, so that we can save multiple cache entries.
When this configuration is turned on, we:
- Automatically create and initialize a CompilePackage on every torch.compile
- Automatically use BundledAutogradcache
- Automatically save the CompilePackage entry to DynamoCache after every compile
You can also use PrecompileContext.serialize() to manually serialize a full object.
I've added unit tests to exhibit this behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155913
Approved by: https://github.com/zhxchen17
This patch effectively ignores traceable_tensor_subclasses, allowing
Dynamo to always try tracing into the `__torch_function__` of tensor
subclass. This helps us with 2 things:
1. allowing users to directly benefit from better compilation of tensor
subclass, by just upgrading pytorch, without having to change legacy
library code (see earlier patches in the stack for examples).
2. potentially exposing more issues in compiling tensor subclass, so we
can get signals and improve them.
As a consequence, it exposed and fixes 2 subtle bugs:
1. In `build_torch_function_fn`, we could get
`torch._C._disabled_torch_function_impl` because we have a
`Parameter` subclass without `__torch_function__` override or if we
have a tensor subclass with `__torch_dispatch__` override. We graph
break on this for now, and plan to add support -- the logic for
simulating `torch._C._disabled_torch_function_impl` is already in
`SuperVariable`, we just need to reuse it.
2. Sometimes we create `SyntheticLocalSource` and need to remove all the
guards installed on it, but we only removed the ones whose source
_is_ the created synthetic source `s`, but forgot about chained
source like `s.foo`, this showed up as
`SYNTHETIC_LOCAL['tmp_0'].__torch_function__.__func__`.
Differential Revision: [D71906141](https://our.internmc.facebook.com/intern/diff/D71906141)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149792
Approved by: https://github.com/jansel, https://github.com/mlazos
ghstack dependencies: #149482, #149483, #149484
This tool makes it easy to search through config state-space with a minimal reproduction or test. It presents a similar interface to the config bisector by taking a test_function that should either raise on Exception or return False upon failure.
It has two entry points: `fuzz_n_tuple`, which tries every combination of n configs, and `bisect`, which randomly flips configs and tries to find the minimal reproduction upon failure. `bisect` is a much more efficient way to search the space, but `fuzz_n_tuple` can give you peace of mind that a new config will compose with every other config.
It's been used to find three bugs so far in the inductor config:
https://github.com/pytorch/pytorch/issues/140220https://github.com/pytorch/pytorch/issues/140219https://github.com/pytorch/pytorch/issues/143524
This PR also adds a bunch of missing types to the inductor config to get them to play nice with the fuzzer, so it can be a good forcing function for adding types to config.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139736
Approved by: https://github.com/eellison
