Update on "[cp] apply fsdp to model when CP is enabled without DP for…

… correct loss and lower mem usage"


**Summary**
Previously CP forgot to shard the model via `apply_fsdp` when DP is not combined with CP. This leads to high peak memory usage and diverging loss.

**Test**
1. modify `train_configs/llama3_8b.toml`
```
steps = 20
context_parallel_degree = 8
```
2.  run training on 8xH100 GPUs
`CONFIG_FILE="./train_configs/llama3_8b.toml" NGPU=8 LOG_RANK=0,1,2,3,4,5,6,7 ./run_llama_train.sh`
Before: CUDA OutOfMemory 
After: successful 20-steps training


[ghstack-poisoned]

torchtitan/parallelisms/parallel_dims.py

@@ -76,7 +76,8 @@ def build_mesh(self, device_type):
         if self.dp_shard_enabled:
             dp_mesh_dim_names.append("dp_shard")
 
-        mesh[tuple(dp_mesh_dim_names)]._flatten(mesh_dim_name="dp")
+        if dp_mesh_dim_names != []:
+            mesh[tuple(dp_mesh_dim_names)]._flatten(mesh_dim_name="dp")
 
         # Mesh for param sharding
         dp_shard_cp_mesh_dim_name = []