[DTensor] Computed DTensorSpec hash lazily (#114322)

This is a forward fix for #113781.

We lazily compute the hash so that we do not try to compute the hash on `SymInt`s (for the stride) during Dynamo tracing.

Tested via:
```
python test/distributed/_tensor/test_dtensor_compile.py -k test_2d_fsdp_tp_ac_compile
```
Pull Request resolved: #114322
Approved by: https://github.com/wanchaol
ghstack dependencies: #113919, #113924, #114134, #113925, #113930, #114141, #113915, #114140

torch/distributed/_tensor/placement_types.py

@@ -388,7 +388,7 @@ class DTensorSpec:
     def __post_init__(self):
         if not isinstance(self.placements, tuple):
             self.placements = tuple(self.placements)
-        self._hash = self._hash_impl()
+        self._hash: Optional[int] = None
 
     def __setattr__(self, attr: str, value: Any):
         super().__setattr__(attr, value)
@@ -397,7 +397,7 @@ def __setattr__(self, attr: str, value: Any):
         if hasattr(self, "_hash") and attr in ("mesh", "placements", "tensor_meta"):
             self._hash = self._hash_impl()
 
-    def _hash_impl(self):
+    def _hash_impl(self) -> int:
         # hashing and equality check for DTensorSpec are used to cache the sharding
         # propagation results. We only need to consider the mesh, placements, shape
         # dtype and stride.
@@ -416,9 +416,12 @@ def _hash_impl(self):
         return hash((self.mesh, self.placements))
 
     def __hash__(self) -> int:
-        # We eagerly cache the spec to avoid recomputing the hash upon each
+        # We lazily cache the spec to avoid recomputing the hash upon each
         # use, where we make sure to update the hash when the `tensor_meta`
-        # changes by overriding `__setattr__`.
+        # changes by overriding `__setattr__`. This must be lazy so that Dynamo
+        # does not try to hash non-singleton `SymInt`s for the stride.
+        if self._hash is None:
+            self._hash = self._hash_impl()
         return self._hash
 
     def __eq__(self, __o: object) -> bool: