Introduce cache hot loading APIs (a.k.a. "Mega-cache") (#143341)

Summary:
This PR essentially introduces two new APIs
* torch.compiler.save_cache_artifacts
* torch.compiler.load_cache_artifacts

which aim to create a mega cache experience where the user can start collecting cache artifacts, and later call the save API to fetch them. In the next attempt, the user can "hot load" the cache artifacts via the load function.

This bundling approach reduces the need to rely on porting individual files one by one, or relying on many network requests.

Note that these APIs CANNOT log to structured logging as these functions will be called before and after compilation, as opposed to during compilation. Due to this limitation, the API returns a struct that the user can log with.

X-link: pytorch/pytorch#143341
Approved by: https://github.com/jansel

Reviewed By: clee2000

Differential Revision: D67927135

Pulled By: oulgen

fbshipit-source-id: 00c4f3955bd098a61b40760a9a29cdf58caf04cf

userbenchmark/dynamo/dynamobench/_dynamo/testing.py

@@ -255,6 +255,11 @@ def __call__(
         self.graphs.append(gm)
         return lookup_backend(self.backend)(gm, example_inputs)
 
+    def clear(self) -> None:
+        self.frame_count = 0
+        self.op_count = 0
+        self.graphs = []
+
 
 # Equivalent to backend="eager", but also records graphs that
 # we can assert on